Je Tiens

Damien Chablat

Philippe Pudlo

Madame Laurence

Anne Guillaume

Stéphane Bouillot

Fabienne Janin De L'ert

Serge Van

Sint Jan

Victor Sholucka

Juan Tomas Celigueta

Sergio Ausejo

Hans-Joachim Ceit

Wirsching

Raphael Bichler

Zohaib, Zhaopeng, Julien Je Pense En Particulier À Sonia

Jeanne Dit «

Julien L Dit

« Loulou

Flo ' Doris

Anne-Laure Cindy

2 COLLABORATIONS BETWEEN RENAULT AND IFSTTAR 3 OBJECTIVES OF THIS WORK 4 STRUCTURE OF THE DOCUMENT

Keywords: Mannequin numérique, Ergonomie, Inconfort, Simulation de posture, Automobile Digital human model, Ergonomics, Discomfort, Simulation of posture, Automotive

Développement des modèles biomécaniques de l'humain pour l'évaluation ergonomique de commandes automobiles -Application à la pédale d'embrayage Résumé Ce travail de thèse s'inscrit dans le cadre du développement des mannequins numériques pour l'évaluation ergonomique de la conception de véhicule, plus particulièrement des commandes automobiles. Il vise à développer des modèles biomécaniques permettant la prise en compte de la dynamique du mouvement et de la force exercée lors d'une tâche pour prédire le mouvement et l'inconfort associé. Ce travail s'est focalisé sur la pédale d'embrayage. Concernant le développement des critères d'inconfort, le concept du mouvement neutre est exploré. Une méthode, basée sur la comparaison entre des mouvements avec des configurations imposées et ceux moins contraintes, est proposée. Elle a permis l'identification de paramètres biomécaniques pertinents et de proposer des indicateurs d'inconfort pour la conception de la pédale d'embrayage. Les relations entre la posture et la force d'appui ont été étudiées expérimentalement en faisant varier le niveau d'effort exercé sur une pédale statique. Nos résultats montrent que la direction d'effort et l'ajustement postural suivent le principe de minimisation des couples articulaires.

Par ailleurs l'utilisation d'un critère de minimisation de l'activité musculaire a montré une amélioration de la prédiction de la direction d'effort pour les efforts peu élevés.

Les indicateurs d'inconfort proposés dans cette étude fournissent des informations objectives permettant aux ingénieurs de conception de comparer des solutions alternatives de design. Le travail sur les mécanismes de contrôle de l'effort et de la posture constitue, quant à lui, une première étape dans l'optique de prendre en compte la force exercée dans la simulation de posture.

Table of content

GENERAL INTRODUCTION

DIGITAL HUMAN MODELS (DHM) FOR ERGONOMICS IN AUTOMOTIVE INDUSTRY

Current

on statistical regression functions from experimental observations of driving postures. About discomfort analysis, working assessment methods such as OWAS [START_REF] Karhu | Correcting working postures in industry: A practical method for analysis[END_REF] or RULA [START_REF] Mcatamney | Rula: a survey method for the investigation of workrelated upper limb disorders[END_REF] are implemented. However, these methods were originally developed by ergonomics experts to assess working postures and/or tasks in industry (Dellman, 2004). In Ramsis™, a posture-prediction algorithm is based on the principle of maximizing the likelihood of joint angles relative to a database of driving postures [START_REF] Seidl | Das Menschmodell Ramsis: Analyse, Synthese, und Simulation dreidimensionaler Korperhaltungen des Meschen [The man-model RASMSIS: Analysis, Synthesis, and simulation of three-dimensional human body postures[END_REF]. More recently, the FOCOPP model [START_REF] Seitz | FOCOPP -An approach for a human posture prediction model using internal/external forces and discomfort[END_REF][START_REF] Wirsching | New enhancements and validation of force based posture and discomfort predictions[END_REF] was proposed for predicting postures implying force exertion. Postures are predicted by minimizing joint load (sum of active joint torques, i.e. due to muscles, and passive joint torques) relative to joint strength (maximal voluntary joint torques, i.e. due to muscular strength and passive joint torques, i.e. due to the body parts' masses). The discomfort could be assessed at each joint from a linear correlation between joint load and joint discomfort [START_REF] Zacher | Strength based discomfort model of posture and movement[END_REF]. The posture prediction and discomfort modeling integrated in Ramsis™ have the advantage to take into account the force applied for predicting posture and discomfort. However, all external forces (magnitude and direction)

have to be known and usually, only the useful component of the force to apply on the control is known. The force direction that a person may apply is not always known. In addition, relevant objective discomfort criteria are difficult to identify using this model.

Collaborations between Renault and IFSTTAR

This PhD thesis follows on a long history of collaborations between the car manufacturer Renault and IFSTTAR on the ergonomic assessment of vehicle interior design. Renault and IFSTTAR started working together on proposing digital tools for the evaluation of the ergonomic qualities of a car design in the late eighties with the development of the digital human model MAN3D [START_REF] Verriest | Application of a 3D human model to computer aided ergonomic design of vehicles, in proc[END_REF]. This DHM allowed the three-dimensional geometric representation of individuals of any anthropometric dimensions and solved geometrical and static problems as reaching. However, like many DHMs, it did not guarantee a realistic representation of the gestural and postural behavior of a human being. Furthermore, the discomfort resulting from interactions with the environment and internal mechanical stresses affecting the musculoskeletal system was not considered. Then both Renault and IFSTTAR focused the development of tools for simulating realistic motion and for predicting discomfort. An important step was the European project REALMAN (2001REALMAN ( -2004) ) in which

Renault and IFSTTAR participated. Following this project, Renault, IFSTTAR and Altran began the development of a motion simulation tool for ergonomic assessment of vehicle interior design called RPx and based on the concepts developed in REALMAN [START_REF] Monnier | RPx: A motion simulation tool for car interior design[END_REF]. This methodology was successfully applied to movements related to vehicle conception such as car ingress/egress [START_REF] Monnier | Motion simulation approach integrated into a design engineering process[END_REF] and driving controls reaching [START_REF] Wang | From motion capture to motion simulation: an in-vehicle reach motion database for car design[END_REF]. The same approach was later extended to car ingress/egress by the elderly with the national project HANDIMAN [START_REF] Chateauroux | A database of ingress / egress motions of elderly people[END_REF]. However, the proposed motion simulation approach remained purely kinematic and the discomfort was evaluated only through the analysis of joint angles during motion, which might not be enough for assessing the force exertion-motions such as pedal clutching or handbrake pulling. To Renault SAS). The main objective of this project was to develop advanced DHM for ergonomic design that can, among other things, assess the discomfort and simulate a population including the effects of age, as well to simulate complex movements taking into account the dynamic constraints of a task (contacts, force exertion ...). This PhD thesis started at the end of the FAC project and was carried out during the whole DHErgo project.

Objectives of this work

This PhD thesis aims at developing biomechanical human models for ergonomic assessment of automotive controls and focused on two issues.

First research question is how to identify the most relevant parameters to assess the ergonomic qualities of a product objectively. Indeed, thanks to long experience in automotive engineering, currently existing automotive controls are generally well designed. From a design engineers' point of view, it is more critical to choose the best solution among already well designed ones than to distinguish a well-designed product from bad ones. We believe that discomfort is induced by interactions with environment and internal biomechanical constraints affecting the musculoskeletal system. In this work, we propose a generic approach by comparing imposed and less-constrained movements for identifying relevant objective biomechanical indicators for evaluating a task.

Then second research question is how to predict realistic postures/movements when operating an automotive control. Despite recent progresses in motion simulation, current DHMs are mainly limited to geometric and kinematic representations of humans. We believe that the dynamic constraints (force requirements, contact forces …) during an automotive control operation have to be considered for posture/movement prediction. For this, we need to understand how force (magnitude and direction) and posture are controlled during such a task.

The two research questions were investigated separately but remained interdependent. As a matter of fact, to be used in the design cycle, an objective evaluation of discomfort requires a realistic motion simulation, and a realistic motion simulation has also to take into account discomfort criteria.

In this framework of developing DHM for ergonomic assessment of automotive controls, we focus particularly on one specific control: the clutch pedal. It is an important control in the European cars, which are mostly manual transmission cars. Besides, pedal could often restrict the posture of the user even more than hand controls and inappropriate pedal design may contribute to muscle fatigue and cause discomfort for users (Sanders and McCormick, 1993).

Although, the approach developed in this work is applied to the pedal clutching task, the purpose is to propose a generic method that could be extended to other tasks.

Structure of the document

At first, existing studies will be reviewed in Chapter 1, focusing on three main items:

discomfort modelling based on biomechanical parameters, experimental studies on automotive controls and especially on pedal control, and posture prediction methods. This chapter aims to have an overview of the current state of the art in the ergonomic assessment of the automotive controls in order to identify the working hypotheses.

Chapter 2 deals with the identification of objective discomfort assessment criteria for the clutching movement. It presents the first case study conducted in the DHErgo project. Based on the results of the motion analysis, a general methodology for developing biomechanical parameters based discomfort indicators is proposed and applied to the selected task.

Chapter 3 deals with the understanding of the control mechanism of force and posture during a clutching movement. Experimental data collected in the FAC project will be analysed.

Based on the experimental observations, simulations using a biomechanical human model and optimization method were performed to explain separately, on the one hand, the control of the pedal force direction and on the other hand, the postural change in function of force exertion level.

Chapter 4 is an exploratory investigation on the potential contribution of the musculoskeletal models for a better understanding of the mechanism of force exertion control. The control of the pedal force direction was simulated using a custom musculoskeletal model and the results

were compared to the one from Chapter 3.

Finally, the last section summarizes the main results of this PhD thesis and gives some perspectives for future researches.

Introduction

In order to develop DHM tools for the ergonomic assessment of the automotive control, it seems important to consider the main characteristics of automotive control design and their effect on a person who manipulates it. From a technical point of view, an automotive control is a (electro-) mechanical device which has to be placed in a more or less predefined area for an efficient use. From the point of view of a user, an automotive control restricts the posture and requires some force level. Then these constraints affect the musculoskeletal system and may cause discomfort. Knowing the possible discomfort sources of a design, some changes can be proposed by the design engineer to improve the perception of the control.

Consequently, in order to improve the ergonomics of an automotive control, a design engineer needs:

Objective discomfort criteria to understand which parameters should be changed to decrease the perceived discomfort Realistic force exertion data (magnitude and direction) on automotive controls Realistic posture when using a control

As a result, this literature review is divided in four sections. First general consideration on discomfort and discomfort modeling methods based on biomechanical parameters were reviewed. Then an overview of the factors influencing the force exertion was proposed. A focus on experimental studies on pedals was also done. The third section reviewed the posture prediction and optimization methods. Finally, this literature review was discussed to extract the work hypotheses of this PhD thesis.

2D i s c o m f o r t modeling

There are a large number of studies dealing with the discomfort in the literature. As the context of this study is the evaluation of discomfort of automotive design, the purpose of this part is not to make an exhaustive review of the discomfort models but rather a general idea of the different models using biomechanical parameters to assess discomfort perception.

Definition of discomfort

From a linguistic point of view, discomfort characterizes the lack of comfort, the prefix "dis-" being a negative sense. However, the concept of discomfort or comfort in itself is more complex to define. Nowadays it is omnipresent in the marketing discourse of consumer goods.

It is also very active in the scientific literature. Indeed, [START_REF] Vink | Editorial: Comfort and discomfort studies demonstrate the need for a new model[END_REF] in an editorial in the journal Applied Ergonomics on the concept of comfort and discomfort has listed nearly 105,000 papers published between 1980 and June 2010 using the term "discomfort" in their title. However, only a few studies attempted to distinguish discomfort and comfort.

Zhang and his colleagues (Zhanget al., 1996;[START_REF] Helander | Field studies of comfort and discomfort in sitting[END_REF] suggested that comfort and discomfort are not two opposing concepts on a continuous scale. Discomfort is primarily associated with biomechanical and physiological factors such as fatigue or pain, while comfort is associated with aesthetics and well-being (Figure 2a). The absence of discomfort does not automatically lead to the comfort and vice versa. One direct consequence of this conceptual distinction between comfort and discomfort is that it should be evaluated separately.

a) b)

Figure Following this same concept, De [START_REF] De Looze | Sitting comfort and discomfort and the relationships with objective measures[END_REF] proposed a model of comfort and discomfort in which he connects the physical environment to perceived discomfort (Figure 2b). This model assumes that the perceived discomfort matches a "dose-response" model used in the study of musculoskeletal disorders in the workplace [START_REF] Winkel | Occupational and individual risk factors for shoulder neck complaints: Part II -The scientific basis (literature review) for the guide[END_REF][START_REF] Armstrong | A conceptual model for work-related neck and upper limb disorders[END_REF] (Figure 3). This model also considers that the external constraints generate an internal disturbance (dose) that triggers a cascade of reactions (response):

chemical, physiological and biomechanical. Moreover, the model assumes that the amplitude of the "answers" is highly dependent on physical capacities of individuals.

Discomfort modeling and biomechanics

Currently, discomfort within DHM is usually assessed using body discomfort assessment methods such as OWAS [START_REF] Karhu | Correcting working postures in industry: A practical method for analysis[END_REF], RULA (McAtamney and Corlett, 1993), REBA (Highett and McAtamney, 2000) and OCRA (Occhipinti, 1998). But, most of these models are more oriented towards postural analysis and were developed initially for the ergonomic assessment of work postures in industry by a panel of experts in ergonomics. Only a rough estimation of posture is usually required from direct visual estimation or from recorded video.

These methods can certainly be helpful for detecting main risk factor of a workplace. But they can hardly be used for ergonomic evaluation of a product such as a vehicle. Therefore, there is a need to develop discomfort predictive models to evaluate task-oriented motions.

From a general point of view, discomfort models have two main objectives. First, they are meant to understand the potential sources of discomfort in order to correct them, and secondly, they are meant to predict discomfort. The latter objective is particularly interesting in the case of the implementation of a predictive method in a DHM. Discomfort models are usually built from data collected using questionnaire and/or rating scales, such as the CP50 scale recommended by Shen and Parson (1997).

Models based on biomechanical parameters are related to the hypothesis that discomfort is a sensation felt during the interaction of the body with the external environment. In general, biomechanical parameters considered are the joint angles, joint torques, muscle forces, etc ...

There are many studies focused on biomechanical parameters for explaining discomfort perception. From a literature review on sitting comfort and discomfort, De Looze et al. (2003) argued that pressure distribution could be an objective measure with the clearest association to subjective ratings. In an experimental study on the automobile clutch pedal operation, Wang et al. (2004) studied the relationship between discomfort and 26 biomechanical parameters when depressing the clutch pedal. The most correlated biomechanical parameters to discomfort ratings were the knee joint work during pedal depression and the heel distance to the floor. Although no predictive models were proposed, the study showed the preference of not raising the leg in the approach phase, the preference of low joint work and moment especially at knee during the depression phase of the clutch pedal operation. [START_REF] Dickerson | The relationship between shoulder torques and the perception of muscular effort in loaded reaches[END_REF] investigated the relationship between shoulder torques and perception of muscular effort in loaded arm reaches. Individual subject torque profiles were significantly positively correlated with perceived effort scores rated using a modified Borg CR-10 Exertion Scale (Borg, 1982). [START_REF] Kuijt-Evers | Association between objective and subjective measurements of comfort and discomfort in hand tools[END_REF] studied the discomfort perceived when using hand tools according to EMG measures and palm pressure distribution and reported that the pressure-time integral was the best predictor of discomfort. More recently, [START_REF] Kee | Relationships between subjective and objective measures in assessing postural stresses[END_REF] investigated the relationship between postural stresses and posture holding time, maximum holding time, torque at joints, lifting index and compressive forces at L5/S1. This study showed in particular a strong correlation between discomfort and compressive forces at L5/S1. Some studies also proposed discomfort predictive models based on biomechanical parameters.

For example, Jung and Choe (1996) developed a discomfort model for arm reaching posture based on joint angles. Based on experimental data, they defined a discomfort regression model in which the independent variables were the 7 joint angles of the upper limb and the weight of the object held with the hand. More recently, [START_REF] Wang | Prediction of in-vehicle reach surfaces and discomfort by digital human models[END_REF] proposed a unified data based approach for DHM to predict both in-vehicle reach capacity and discomfort.

But most of these predictive models are specific and their application ranges are limited by the experimental conditions. However, some researchers attempted to propose generalized biomechanical based discomfort models, considering that for any movement, the perceived discomfort may be a combination of discomfort feelings of all DoFs involved in the motion.

Bubb and his colleagues [START_REF] Bubb | Research for a strength based discomfort model of posture and movement[END_REF][START_REF] Zacher | Strength based discomfort model of posture and movement[END_REF] attempted to identify joint angle and torque related discomfort functions for most of the joints of the human body. Kee andKarwowski (2001, 2003) defined joint angle based iso-comfort functions and proposed a ranking system for evaluation of joint motion discomfort. Using a similar approach, [START_REF] Chung | Quantitative postural load assessment for whole body manual tasks based on perceived discomfort[END_REF] proposed a postural load assessment method for whole body manual tasks from postural classifications of body parts. However, these approaches are very dependent on the availability of data on the perception of discomfort in terms of joint angles and joint torques, and on the representativeness of the subjects' sample on which the model is based.

Finally, generalized discomfort models should be differentiated from generic approaches used to define task-related discomfort functions. [START_REF] Dufour | Discomfort assessment of car ingress/egress motions using the concept of neutral movement[END_REF] proposed the concept of neutral movement for identifying discomfort function and applied it to the analysis of the discomfort of car ingress/egress movement (see also Wang, 2008b). The concept is based on the assumption that ideally, the neutral movement for a person should be the one which generates the least discomfort for a task and could therefore be considered as a motion reference. The basic idea is to define a corridor for each considered biomechanical parameter (joint angle for example, Figure 4), which reflects the intra-and inter-individual variability of the neutral movements. Any deviation from the neutral movement corridor due to environment constraints may reveal a possible discomfort perceived during a movement.

Nevertheless the main limit of this concept is that the evaluation of discomfort depends greatly on the definition of the neutral movement. if the corresponding strength values of the individuals are to be compared (Mital et al., 1998).

In addition, many studies [START_REF] Kroemer | Die Messung der Muskelstärke des Menschen. Methoden und Techniken[END_REF][START_REF] Ayoub | Development of an Atlas of Strengths and Establishment of an Appropriate Model Structure. Anatomy and physiology human factors engineering & man and machine system[END_REF][START_REF] Daams | Isometric Pull-Push Strengths in workspace: 1 Strength Profiles[END_REF]Mital et al., 1998;[START_REF] Kumar | Muscle strength[END_REF] suggested that the assessment methods can also strongly affect muscle strength data. The following factors are generally mentioned:

Type of force exertion: static isometric, isokinetic dynamic ... with an imposed or perceived by subjects force level

Test conditions: measuring equipment, type of postural stress (straps, handles ...), instructions, posture of the subject, number of repetitions of a trial, motion speed …

Muscle fatigue: experimental duration, duration of rest, period in which the measurement was made (morning, afternoon ...)

Subject: physical and psychological state at the time of the experiment Thus, it is important of an extensive and detailed description of the experimental method focusing on all these factors to acquire muscle strength data. Moreover, the assessment of strength should be performed under conditions as close as possible as the specific task situations.

Pedal force exertion

First strength capability studies usually gave recommendations for the design of specific controls. For example, based on the maximum brake pedal force from the 5th percentile female group in terms of stature , [START_REF] Mortimer | Brake force requirement study: driver-vehicle braking performance as a function of brake system design variables[END_REF] suggested the maximum braking from capability from a passenger car should not exceed 400N. [START_REF] Kroemer | Foot operation of controls[END_REF] showed that maximum static forces of sitting operator depended on seat type, pedal type and position. His review showed that strong pedal force could be exerted when a suitable backrest is provided and when the pedal is placed almost at seat height and at a distance requiring low knee flexion angles (between 20° and 40° of flexion). A foot control position often restricts the posture but small variation in lower limb joint angle has also an impact on the force output. According to [START_REF] Lara-Lopez | Measurement of forces applied to handgrips and pedals for a sample population of Mexican males[END_REF], changes in the knee angles affected maximum static forces exerted on a foot pedal by seated subjects. The smaller the knee flexion angle (from 20° to 80°) is, the greater the foot strength is. [START_REF] Mehta | Leg strength of Indian operators in the operation of tractor pedals[END_REF] agreed with this observation and stated that maximum leg strength was achieved when the leg was in almost elongated position (i.e. knee flexion angle between 35° and 45°).

The capability of the foot to create force can also be influenced by the direction of the applied force or moment [START_REF] Das | Isometric pull -push strengths in workspace: 2 Analysis of spatial factors[END_REF]. [START_REF] Pheasant | Human strength in the operation of tractor pedals[END_REF] showed, for example, that foot strength depended on foot thrust direction. For different pedal locations, the force in the direction of the hip (from the hip to the heel) was greater than the force in the direction of the knee (from the knee to the heel).

Subjects' characteristics and psychological parameters can influence the force exertion. [START_REF] Mortimer | Brake force requirement study: driver-vehicle braking performance as a function of brake system design variables[END_REF] measured foot brake pedal force capability on a large sample of female and male drivers. Interestingly, verbal encouragement tended to increase the pedal force capability of the subjects. Besides, women were found significantly weaker than men. [START_REF] Pheasant | Human strength in the operation of tractor pedals[END_REF] agreed with this observation reporting that average strength of the females was 81% of the average male strength. It was also reported that left and right legs had the same strength.

In case of tractor pedal, [START_REF] Mehta | Leg strength of Indian operators in the operation of tractor pedals[END_REF] and [START_REF] Fathallah | Forces required to operate controls on farm tractors: Implications for young operators[END_REF] showed that the recommendations on pedal force limits from the International Organization for Standardization were conflicting with the foot strength capability of specific tractor operator population, in particular Indian operators [START_REF] Mehta | Leg strength of Indian operators in the operation of tractor pedals[END_REF] and under 17-year old aged operator [START_REF] Fathallah | Forces required to operate controls on farm tractors: Implications for young operators[END_REF].

Knowing the maximum strength is necessary but may not be sufficient for pedal design.

Considering that pedals should have a low resistance to at least to overcome the effects of vibration and gravity [START_REF] Southall | The discrimination of clutch-pedal resistances[END_REF], it may be interesting for pedal design to determine the minimum acceptable pedal resistance. This aspect may be particularly important for the clutch pedal operation according to [START_REF] Wang | Experimental investigation and biomechanical analysis of lower limb movements for clutch pedal operation[END_REF]. However, few studies focused on automotive pedal force perception in the literature. [START_REF] Wang | Pedal operation[END_REF] reported a study by [START_REF] Mick | Eine experimentelle Untersuchung der Korrelation von Komfortempfinden und Handhabungskräften an Pedalen[END_REF] on the relationship between the subjective sensation and the force applied to a pedal in order to determine the upper force limit for a force feedback-based active accelerator pedal. A minimum pedal resistance of 40 N was recommended for the accelerator based on the results of the experiment. However, only 12 subjects (6 females and 6 males) participated in this study. In addition, the recommendation is only relevant for pedal operation with the heel supported on the floor.

Another aspect of the pedal force exertion is how the force direction is controlled by the user.

Few studies in the literature investigated the mechanism of the control of the force applied on a pedal. In case of clutch pedal operation, [START_REF] Wang | Experimental investigation and biomechanical analysis of lower limb movements for clutch pedal operation[END_REF] observed experimentally that the direction of the pedal force at the end of the pedal depression in the sagittal was highly dependent on the direction connecting the hip joint and the contact point. Using a 2D biomechanical model, pedal force directions were predicted by minimizing the lower limb joint torques and were found in agreement with the experimental observation. A similar approach was used by [START_REF] Schmidt | Foot force direction in an isometric pushing task: prediction by kinematic and musculoskeletal models[END_REF] to understand the control of the foot force direction when pushing a fixed bicycle pedal. Pedal force direction was predicted using on the one hand, a 2D biomechanical model and joint torque minimization, and on the other hand, a musculoskeletal model and foot force maximization. Both approaches presented strong similarities and predicted force directions close to the hip-pedal direction were predicted as in experimental data. Schmidt et al. also showed that in case of bicycle pedal, the use of a musculoskeletal model was a better prediction of the force direction regarding the experimental data. Interestingly, even with different postures (seated on a bicycle and seated in a car) and thrust force directions (mainly vertical for bicycle and horizontal for car pedals), both studies suggested that the control of the foot force may follow the principle of reducing joint load.

Posture prediction method

Posture prediction is a key functionality of a DHM used in ergonomic assessment of a product or a workplace. Indeed, an unrealistic predicted posture may lead to wrong analysis and so, wrong ergonomic recommendations. Every DHM package used for ergonomics include an inverse kinematics (IK) solver which will compute a posture for the manikin to reach the goal from a given target position of a hand or foot. However, inverse kinematics alone produces a feasible posture, not necessarily a likely or realistic posture that a person may adopt. There exist a large variety of methods to simulate human-like postures and motions. The aim of this section is not to cover all the existing inverse kinematic solvers used to predict a posture or a motion but to focus on the methods that could be considered for a force-exertion posture prediction. Two main families of posture prediction methods were distinguished: knowledgebased methods and data-based methods (Wang, 2008a).

Knowledge based methods

The knowledge-based methods assume that the motion control strategies are known as optimization criteria or heuristic rules. The general approach is to find the posture that minimizes (or maximizes) an objective function while considering the task constraints and some physiological restrictions (joint limits for example).

Single-objective optimizations were widely used to predict a posture or a movement. [START_REF] Uno | Formation and control of optimal trajectories in human multi-joint arm movements: Minimum torque-change model[END_REF] proposed an arm reaching movement based on the minimization of the change in joint torques during motion. [START_REF] Soechting | Moving effortlessly in three dimensions: does Donders' law apply to arm movement[END_REF] showed that minimizing the amount of work to move the arm from its origin was successfully predicting the final reaching posture.

Based on a discomfort predictive model using joint angle cost functions, [START_REF] Nationale De Mensuration | Human reach posture prediction based on psychophysical discomfort[END_REF] predicted an arm reaching posture by minimizing the discomfort associated with a pointing task. Also for an arm reaching posture, [START_REF] Wang | A geometric algorithm to predict the arm reach posture for computer-aided ergonomic evaluation[END_REF] proposed a geometric inverse kinematics algorithm based on the minimization of the norm of the joint angular velocities.

More recently, some multi-objective optimization (MOO) methods were proposed. The objective of the MOO methods is find a set of design variable q in order to minimize n objective functions f i (i = 1, …, n) simultaneously (Eq. 1) subject to equality and inequality constraints (Eq. 2):

With m, the number of inequality constraints p, the number of equality constraints MOO was used by the VSR (Virtual Soldier Research) group using three objective terms of human performance measures: potential energy, joint displacement and joint discomfort [START_REF] Yang | Multi-Objective Optimization for Upper Body Posture Prediction[END_REF]. This approach was tested on many tasks such as reaching, climbing, walking, box lifting and seating [START_REF] References | Physic-Based Digital Human Modeling: Predictive Dynamics[END_REF][START_REF] Kim | Multi-objective Optimization Approach for Predicting Seated Posture Considering Balance[END_REF][START_REF] Marler | Optimization-Based Posture Prediction for Analysis of Box Lifting Tasks[END_REF]. Using a similar approach, [START_REF] Ma | Multi-objective Optimization Method for Posture Prediction and Analysis with Consideration of Fatigue Effect and Its Application Case[END_REF] predicted a drilling assembly operation posture considering the muscular fatigue and the discomfort as objective functions.

Knowledge-based methods have the advantage of being able to take into consideration as many objective functions as necessary to describe a motor control strategy. But one issue of these methods is that multiple objectives may conflict between them (e.g., what minimizes one function may increase another). One solution is to convert the MOO problem in a singleobjective problem using a weighted sum of the different objective functions [START_REF] Yang | Multi-Objective Optimization for Upper Body Posture Prediction[END_REF][START_REF] Ma | Multi-objective Optimization Method for Posture Prediction and Analysis with Consideration of Fatigue Effect and Its Application Case[END_REF][START_REF] Marler | Use of Multi-objective Optimization for Digital Human Posture Prediction[END_REF]. Then the issue lies in the estimation of the relative weights or priorities to assign to each objective. Maybe the weights depend on the preference of the individual. Finally, the major limitation of these posture prediction methods lies in the identification and validation of the objective functions.

Data based methods

Unlike the knowledge-based approach, the data-based methods require little knowledge on motor control strategy. This approach uses existing posture data to predict a new posture. The realism of the simulation mostly depends on the richness of the database.

One method is to propose a statistical approach to predict posture from experimental data in a function of task and operator characteristics. [START_REF] Seidl | Das Menschmodell Ramsis: Analyse, Synthese, und Simulation dreidimensionaler Korperhaltungen des Meschen [The man-model RASMSIS: Analysis, Synthesis, and simulation of three-dimensional human body postures[END_REF] [START_REF] Chaffin | Development of computerized human static strength simulation model for job design[END_REF]. This algorithm defines whole-body postures by predicting body segment positions based on hand location and orientation (supine, neutral, or prone), and worker height and weight [START_REF] Hoffman | Predicting force-exertion postures from task variables[END_REF]. Thus the effects of hand force on posture are not reflected in model predictions. [START_REF] Faraway | Modeling reach motions using functional regression analysis[END_REF] developed a statistical functional regression model for prediction human reach motion. Based on a large set of reach motion data, the model aimed to predict the reach motion trajectories of a DHM based on the subject's characteristic (age, gender, stature …) and on the target location. [START_REF] Reed | A statistical method for predicting automobile driving posture[END_REF] used a similar approach in the Cascade postureprediction model to predict realistic driving posture. More recently, [START_REF] Hoffman | Whole-body postures during standing hand-force exertions: development of a 3D biomechanical posture prediction Model[END_REF] developed a statistical posture-prediction model for standing hand force exertions (Figure 6).

In this model, the magnitude and the direction of the force were considered as inputs with the subject's characteristics and the target location. Another approach proposed by [START_REF] Zhang | A Three-Dimensional Dynamic Posture Prediction Model For Simulating In-Vehicle Seated Reaching Movements: Development and Validation[END_REF] predicted in-vehicle seated reaching movement using inverse kinematics in combination with experimental data. Based on weighted pseudo inverse, this method uses optimization to determine the weighting factors such that the predicting joint angular motion trajectories have minimum deviation from the joint angle trajectories of a real human reach motion. [START_REF] Seitz | FOCOPP -An approach for a human posture prediction model using internal/external forces and discomfort[END_REF] also proposed an optimization-based approach for posture prediction called the FOCOPP model. This model is based on human posture and strength data and aims to predict the posture that minimizes the joint load due to a task. The FOCOPP model is presented as a generalized posture prediction model as it is not based on task-related data but on functional capacities data.

Finally, posture and motion prediction can also be accomplished by modifying motioncapture data to conform to the requirements of the task [START_REF] Park | Toward memory-based human motion simulation: development and validation of a motion modification algorithm[END_REF][START_REF] Monnier | Motion simulation approach integrated into a design engineering process[END_REF][START_REF] Monnier | RPx: A motion simulation tool for car interior design[END_REF]. This method has been implemented in the software RPx for the car manufacturer Renault. Globally, the process consists in three steps:

Discussion and work hypotheses

This literature review focused on the design engineer's expectations in terms of ergonomic assessment of the automotive controls. The main limitations of the current DHMs concern the discomfort modeling and the posture prediction, the two issues being closely related.

First, discomfort is a complex process which is primarily associated with physiological and biomechanical factors. The need of a design engineer in terms of discomfort modelling is not only to get a rating score but also to get insights about the sources of discomfort. But how to identify objective discomfort criteria remains to be one of challenging research issues. Any task oriented motion is more or less-constrained by the environment and thus require specific objective discomfort criteria. A generic approach to define the discomfort criteria should be considered so that it could be applicable to a large range of tasks. Based on these considerations, the concept of neutral movement proposed by [START_REF] Dufour | Discomfort assessment of car ingress/egress motions using the concept of neutral movement[END_REF] is an interesting approach. Assuming that a better comfort may be obtained when people can make their own appropriate adjustments, these less-constrained motions can then be used as reference data for comparing a proposed solution and for identifying objective discomfort criterion based on biomechanical data. A five-step generic approach for the ergonomic assessment of a task-related motion is proposed (Wang et al., 2011). Based on the neutral movement concept, it consists in:

1) Identify the main critical design parameters

2) Plan an experimental design with motion and force measurements, discomfort evaluation and an experimental mock-up allowing the participants to easily choose their preferred adjustments

3) Conduct the experiment with voluntary participants 4) Process and analyze the data (subjective perception and motion analysis)

5) Identify relevant biomechanical parameters for discomfort assessment and define ergonomic criteria

In this study, the clutch pedal operation was chosen to investigate how the proposed approach could be helpful for improving the automotive control design when using a DHM tool. The data of the clutching movements collected in DHErgo project will be used for illustrating this approach.

Then the literature review clearly shows the dependency between force exertion and postural behavior. Tasks conditions (force requirements, force direction, type of control, control position …) have a major influence on both force exertion and posture. As a result, the assessment of strength and posture when operating an automotive control should be performed under conditions as close as possible to the specific task. Maximum force exertions on a control are useful to provide force limit recommendations. But force perception may also be important to anticipate how the users would feel the control during its use. Besides, it can be assumed that according to the level of effort required for manipulating a control, postural adjustments are required in response to force solicitation. Then experimental investigations of the force exertion on control are necessary to understand the mechanism of control of force and posture. Understand the motor strategies that rule the behavior during a task is a necessary step to be able to predict realistic posture and force direction. Force control and posture prediction were rarely studied together in the literature. It was suggested that the motor strategy that controls the foot force may follow the minimization of the joint load. But, all these studies were performed in 2D whereas, as a clutch pedal is not fully aligned with the left hip joint, the force direction should be 3D, especially for high force exertion level. Few methods focused on force exertion-posture prediction. But only MOO methods have the advantage to be able to explain postural strategy, as they consider motion control strategies as optimization criteria to predict the posture. Interestingly, optimization methods seem to be suitable to explain both force control and postural behavior. In this study, the data from the FAC project are used to investigate the force exertion on automotive controls and especially on clutch pedal. All the aspects from force capability to the mechanisms of force and posture control of this task are going to be investigated in order to propose improvements for realistic force exertion-posture prediction using a DHM.

Chapter 2: Using less constrained movement to identify objective discomfort assessment criteria

Introduction

Thanks to recent progress in motion simulation, simulating a complex tasked orientated motion now becomes possible. But another challenging issue for DHM is how to assess motion related discomfort. Generally speaking from the design engineers' point of view, it is more critical to choose the best designed product among already well designed ones than to distinguish a well-designed product from badly ones. Because any task oriented motion is more or less constrained by the environment, a better comfort may be obtained when people can make their own appropriate adjustments. These less-constrained motions, also referred to "neutral" motions by [START_REF] Dufour | Discomfort assessment of car ingress/egress motions using the concept of neutral movement[END_REF], can then be used as reference data for comparing a proposed solution and for identifying objective discomfort criterion based on biomechanical data. This biomechanical approach for evaluating motion related discomfort using the concept of less-constrained movement was adopted in the European project DHErgo. In this PhD thesis, the work was centered on the data from the case study on clutch pedal operation of DHErgo. As a case study, the aim of the experiment performed in the DHErgo project was not to investigate all the spectrum of pedal design parameters. Actually, the objective of this experiment was to investigate the concept of less-constrained movement introduced by [START_REF] Dufour | Discomfort assessment of car ingress/egress motions using the concept of neutral movement[END_REF] and its effects on clutching movement and discomfort perception. Only a few studies of the comfort of pedal operation were reported in existing literature (see [START_REF] Haslegrave | Factors in the driving task affecting comfort and health[END_REF][START_REF] Wang | Pedal operation[END_REF]. In past studies [START_REF] Wang | Experimental investigation and biomechanical analysis of lower limb movements for clutch pedal operation[END_REF]Wang et al., 2004), four design parameters of the clutch pedal (seat height, pedal travel length, pedal travel inclination, pedal resistance) were investigated to understand their effects on lower limb movements and pedal discomfort. Although some design recommendations for pedal design were provided by Wang et al. (2004), ergonomic criteria for pedal design which can be easily used by design engineers are still missing. The purpose of this study is therefore to compare less-constrained clutching motions with normally constrained ones in order to identify objective discomfort assessment criteria for the clutch pedal operation that can be easily implemented in a DHM.

The study was organized in three parts. First the discomfort perceived during the clutch pedal operation experiment performed in DHErgo project was analyzed. Then the biomechanical analysis of the clutching movements collected was performed. Finally, the results of both analyses were discussed and some discomfort indicators for the clutch pedal operation were proposed.

Material and methods

The data used in this chapter were collected in the European project DHErgo. The detailed description of the data collecting protocol was given in D17 ( 2010) for the first case study on pedal clutching task.

Subjects

Twenty volunteer subjects took part in the experiment. They were divided in four anthropometric groups according to age and gender and stature. As the size of these samples was very small, homogeneous groups were recruited. Subject selection criteria were based on:

Age. Two age groups were chosen:

o Young subjects: between 20 and 35 years o Older subjects: between 65 and 80 years.

Stature.

Due to small sample size, only the 50th percentile value of the French population (IFTH, 2006) for each age-gender group in terms of stature was considered in order to get more information on age effects (Table 1). Physical conditions. To recruit homogenous groups with functional capacities representative of the global population, the subject should not be athletics. Therefore, they should not be or have been high level sportsmen (or women), meaning that they should not be trained for national and international competitions. Then, people (young and older subjects) should not have had any history of trauma or serious disease, or particular known orthopedic (arthritis) or neurologic disorders. They should not undergo any medical treatment. With ageing, older people may have a reduction of their functional performance. As the experiments were physically demanding, the older subjects should be fit enough to produce all the maximal strength and ranges of motion measurements without any problem. The first condition was to recruit older people who do not feel any difficulty during the activities of the daily life. A questionnaire was used to recruit the older subjects.

Driving experience. All subjects were regular drivers with 2-year minimum driving experience.

The experimental protocol was approved by the ethical committee of Ifsttar. Informed consent was given before participating in the experiment.

Forty anthropometric dimensions were measured for all participants. They are described in Appendix. Functional tests were performed on each subject in order to characterize joint ranges of motion and maximum isometric joint torques. The tests were selected according to the relevancy to clutch pedal operation. Therefore, maximum isometric joint torques and ranges of motion were collected only for the left lower limb.

The active ranges of motion (ROM) were measured for the left hip, knee and ankle joints in flexion/extension, abduction/adduction (except knee) and axial rotation (except ankle). Except for hip flexion/extension and abduction/adduction ROMs performed standing; all ROMs were performed sitting on a stool (Figure 8). Maximum isometric joint torques were performed for the three lower limb joints only in flexion/extension using a specifically designed ergometer. Each joint was tested randomly at different joint positions estimated with a goniometer (Figure 9). The angles were defined from the standing posture according to the recommendations by [START_REF] Gallagher | Physical strength assessment in ergonomics[END_REF] (see also [START_REF] Chaffin | Occupational Biomechanics, 3 rd Edition[END_REF]: ankle flexion of 0°, knee flexion of 45° and hip flexion of 60°. Hip measurements were taken with the subject standing and leaning on a back support. Knee and ankle measurements were taken with the subject sitting in a rigid seat. The means and standard Append

E

2.2.i

A ch pedal he other adjustments such as pedal travel length or the clutch pedal lateral position were done manually. Moreover, the lateral adjustment of the clutch pedal was not continuous but incremental. The range of adjustment was 60 mm with an increment of 10mm. The same linear spring device was used for all pedal configurations to generate pedal resistance during the pedal depression. However, in order to match the initial angle of the clutch pedal with horizontal plane, different pre-loadings were applied to the device. As a consequence, for a similar pedal travel length, the force needed to fully press the pedal may differ.

2.2.ii Motion capture

The movements of all participants were recorded using the opto-electronic motion capture system VICON with ten MX T40 cameras sampling at 100 Hz. The 3D trajectories of 40 reflective markers attached to participant were measured (location of the markers described in Appendix). In order to be able to recreate the mock-up geometry, 35 markers were put on it.

Special attention was paid to the clutch pedal with 6 markers on the device to measure trajectories and pedal orientation during the depression.

2.2.iii Force and seat pressure measurement

A TME © uni-axis force sensor with a capacity of 2000 N was used to record the force applied by the subject on the ergometer. A TME 3-axis force sensor with a capacity of 1500 N on each axis was used to record the force applied by the subject on the clutch pedal during the clutching movement (Figure 11a). All force sensors were synchronized with VICON © system and sampling at 1kHz.

Two pressure maps (XSensor © X3Pro PX100) with pressure range 10-200 mmHg were also installed on the seat (one on the backrest, one on the seat) to qualitatively examine the pressure distribution during clutch pedal operation (Figure 11b). cause of different pre-loading applied to the spring device in order to match the initial angle of the clutch pedal with horizontal plane.

2.3.ii Discomfort assessment

Discomfort feelings when clutching were collected, for all trials, through a questionnaire, which was composed of two parts (see in Appendix). At first, multiple-choices questions were used for assessing the seat (Q1), the pedal design parameters (pedal position at beginning and end depression, travel length, travel angle -Q2 to Q5). Then, pedal force perception (Q6) and discomfort (Q7) were evaluated using two different rating scales. The perception of the clutch pedal force was assessed using the Borg's CR-10 (Borg, 1998). Discomfort was evaluated using a modified CP-50 category partition scale [START_REF] Chevalot | An experimental investigation of the discomfort of arm reaching movements in a seated position[END_REF]. In order to prevent from the misuse of the rating scale, three trials were tested by the subjects for training purpose (Table 3). They consisted of two extreme configurations a priori very uncomfortable (CAL1 and CAL2) and one average configuration considered as less uncomfortable (CAL3).

Two extreme configurations were: one combined a high seat height, a small travel inclination angle and a long travel length and the other combined a low seat height, a high travel inclination angle and a long travel length. The average configuration had average pedal design parameter values.

Experimental procedure

A typical experiment sheet of this experiment is presented in Appendix.

The experiment began with the measurement of the main subject anthropometric dimensions.

Anatomical landmarks were palpated on pelvis and left lower limb according to the procedure described by [START_REF] Van Sint | Color atlas of skeletal landmark definitions[END_REF]. In addition, reflective markers were put on these same points after manual palpation. Photos from two orthogonal views synchronized with a VICON motion capture were recorded in a calibrated environment.

Prior to the clutch pedal experiment, the data for characterizing subject's individual physical capacity of the left lower limb were collected. For each joint ROM, the subject was asked to move slowly and to repeat the motion three times. For each joint torque, the subject was asked to exert a maximum voluntary joint torque and to maintain it during 5s. Also, as recommended in the literature [START_REF] Kumar | Muscle strength[END_REF], two trials will be recorded for each configuration. The highest effort value of the two trials will be taken as the maximum joint torque. Prior to the two trials the subject will be allowed to practice in order to get familiar with the task.

Then the subjects were invited to sit down in the experimental mock-up and to familiarize themselves with the available adjustments and with the discomfort rating scale. For each imposed pedal configuration, participants were asked first to adjust the horizontal seat position with respect to the accelerator pedal and then to adjust the steering wheel. an experimenter changed the pedal position, putting it out of reach of the subjects' foot. Then subjects were asked to choose their preferred pedal position while keeping all other parameters unchanged. Only the pedal position could be modified. In the longitudinal (x) and vertical (z) directions, the pedal adjustment was made by subjects themselves using a remote controller and the range of adjustment was superior to 100mm. In the lateral direction (y), the adjustment was made with the help of an experimenter in a range of 60 mm with a 10 mmincrement. The rest of the parameters such as travel angle or travel length for example remained the same between imposed and less-constrained configurations. This adjustment process continued until that a preferred clutch pedal position was found.

For all trials, before recording the clutching movement, the subjects were asked to fill in the discomfort questionnaire. Then, only one complete clutch pedal operation was recorded. The subjects were instructed to start motion with a standard driving posture (the left foot on the foot rest, the right foot on the acceleration pedal and the hands on the steering wheel), to press the clutch pedal with the left foot without changing the positions of the right foot and hands, then to maintain the clutch pedal fully pressed for about 3 seconds, finally to release the pedal and put the left foot back on the foot rest.

During the pedal position adjustment process, the subjects were able to perform as many times of pedal depression as necessary. The whole duration of the experiment lasted about four hours and a half. A pause of at least 10 minutes was imposed in the middle of the experiment during which drinks and biscuits were provided.

Data processing

At the end of an experimental session, following data types were available:

Anthropometric measurements for each participant algorithm can be only applied on a set of markers belonging to a same body segment when at least three markers attached to this same body are seen. Moreover, this algorithm assumes that the bodies are rigid and that no relative motions between markers exist. This assumption can be merely true in case of markers placed on human skin.

Ramsis™ (Human solutions GmbH, Kaiserslautern) manikin was used. A digital avatar of each participant was created using the measured anthropometric dimensions and the BodyBuilder module from Ramsis. This module allows the creation of a personalized manikin from 21 anthropometric dimensions measured on participants such as stature, weight, width between two shoulders, waist circumference and calf circumference etc... The dimensions which are not collected can be determined using statistical regression integrated in the module.

Then the manikins were superimposed on the calibrated photos using a DLT algorithm integrated in RPx software [START_REF] Monnier | RPx: A motion simulation tool for car interior design[END_REF]. If the superimposition was not satisfying, the manikin's dimensions were adjustable manually directly in RPx (Figure 15). The calibrated photos were taken synchronously with VICON motion captures. When personalized manikin is in the right posture and at the right dimensions, the motion captures are used to define markers coordinates in local body segment coordinate systems.

2.5.ii Motion reconstruction

The whole body motions were reconstructed using RPx by an inverse kinematic approach which calculates the joint angles from captured trajectories of skin markers by minimizing the distance between the captured and model-based markers positions [START_REF] Ausejo | Motion capture and human motion reconstruction[END_REF].

The algorithm implemented in RPx is a global iterative method. The problem is constrained on the one hand, by the kinematic model of the DHM, i.e. Ramsis™ (see in Appendix for details on the kinematic model) in this case and, on the other hand, by the measured markers trajectories. The method is global because it reconstructs the motion of the whole body at once while guaranteeing joint constraints between the body segments. And the method is iterative because it reconstructs the posture considering successively the body segments as a hierarchical tree structure. The advantage is that it reduces a complex problem involving all segments into easier sub-problems. For lower and upper limbs, the motion reconstruction is often an over-guided problem because there is redundant information, i.e. more markers than strictly necessary, to define the posture of the kinematic chain at each instant. On the contrary, for the spine, it is hardly possible to estimate the spine motion using external markers and therefore the problem is under-guided. [START_REF] Monnier | Coordination of spine degrees of freedom during a motion reconstruction process[END_REF] implemented a coordination law between the joint angles of the spine in RPx to avoid unrealistic posture.

Joint forces and torques were computed from motion kinematics data, external forces and inertial properties of the body segments, using a 3D inverse dynamics method based Newton-Euler algorithm and homogeneous matrices (see for exemple, [START_REF] Doriot | A three-dimensional kinematic and dynamic study of the lower limb during the stance phase of gait using an homogeneous matrix approach[END_REF]). The computation is done iteratively at each frame by isolating the body segments from the most distal to the most proximal. The computed joint torques are the sum of all torques acting on the segment, i.e. the torques due to gravity, to motion kinetics, to contact forces and to muscular forces. Body segment properties (mass, centre of mass, inertia) are calculated from subjects' anthropometric dimensions using the regression equations of Dumas et al. [START_REF] Dumas | Adjustments to Mc Conville et al. and young et al. body segment inertial parameters[END_REF]. In case of DHErgo experiment, only the force applied on the clutch pedal was measured and it was decided to apply it to the left foot.

2.5.iii Motion key frames

Two main key instants were identified for analyzing the clutching movement: the beginning of pedal depression, i.e. when the left foot start depressing the clutch pedal, and the end of pedal depression, i.e. when pedal end travel is reached. The first one was defined as the first frame when the force recorded by the pedal sensor was above 5% of the maximal force recorded in the trial. The second one was defined as the first frame when the left foot velocity was below 5% of the maximum velocity recorded after the beginning of pedal depression in the trial. These specific instants or key-frames were determined for each recorded motion.

Postures and forces were analyzed only at these key-frames.

2.5.iv Pedal force and maximum isometric joint torque treatment

The pedal force was analyzed without post treatment. However, two subjects (16_ED and 18_AC) were excluded from the dynamic part of the motion analysis because the force patterns were inconsistent. Indeed, the measurement channels were wrongly configured.

For the maximum isometric joint torque, the plateau method was used. The subject was asked to apply a required force level during 5 seconds. The average between 1.5 to 4.5 seconds was calculated. A counter-weight was added on the ergometer for each measurement so that the device remained in the determined angular position without external effort from the subject.

The force transmitted by the cable was then put to zero. Therefore, only the muscular force applied by the subject was measured without body weight effect. The active muscle joint torque, called also net joint torque (NetJT), can be estimated by: when applied force direction is in the opposite direction of body weight when applied force direction is in the same direction of body weight

Where F is the measured external force, F R is the measured resting force due to the body segment weight when the joint segment remains in the determined angular position without further muscle force activation and R is the radius of the ergometer's wheel (159.5 mm) used for angular positioning. In case that the body weight increases the cable force, then the body weight effect has to be removed for calculating the active joint torque NetJT. When required force direction is in opposite of body weight direction, then moment due to body weight has to be added.

Evaluation of the motion reconstruction

In this part, the goal is to assess the quality of the motion reconstruction. For the kinematic reconstruction, a visual inspection of the reconstructed motions was first carried out. It allows an inspection of visual quality of reconstructed motions. The largest error of motion reconstruction was related to the loss of the markers placed on the anatomical points of the pelvis. The manikin is positioned in space from these markers. So if they disappear, the algorithm has difficulty in positioning the manikin, resulting in jump or vibration of the pelvis. Then, the residues corresponding to the distance between the recorded marker position and the reconstructed one were calculated. For the dynamic reconstruction, the evaluation of the quality of the dynamic reconstruction was hardly possible. Indeed, all contact forces between the individual and the environment are required to verify the force and moment equilibriums at each instant. But the forces applied by the participants on the seat and on the floor with the right foot were not recorded. Moreover, the seat/thigh contact may affect the estimation of the left hip torque by the inverse dynamics procedure. The pressure map data were therefore analyzed visually and a raw estimation of the contact force between the seat and the left thigh as well as the resulting moment on the hip was done.

2.6.i Kinematic reconstruction evaluation

The visual analysis of the reconstructed motions showed no major problem of marker losses.

Indeed, four markers were placed on the pelvis (left and right anterior superior iliac spine, left and right crest tubercle) and they were all missed for only 1 trial. The labeling of the markers was presented in Appendix.

The mean residue for all markers and trials was 11.7 ± 3 mm. The largest errors were obtained for a marker on the thigh (LTH3MK) and a marker on the left calcaneus (LFCCMK) with respective mean values of 16.7mm and 16.5mm (Figure 16). These errors were particularly due to relative motion between marker and body segment for LTH3MK (this marker was in contact with the seat during clutch pedal depression) and to marker displacement or loss for LFCCMK (this marker was often removed during motion and not exactly replaced the same.

Moreover the obstructed environment made it hard to see by the cameras). However, it can be noted that the mean residues of this two markers were still reasonable. The other markers showed relatively reliable residue below 15 mm.

2.6.ii

The pre pressure the hyp could n subjects (REN3) the resulting moment on hip was performed at the end of the pedal depression. That motion key-frame was selected because the left leg should be the most extended at the end of the pedal depression and so it should be the instant when the most contact between the thigh and the seat occurred. Then it should be considered that no reflective markers were put on the pressure maps. As a consequence, it was considered that the pressure maps were always in the same position between trials and subjects. The following considerations were done for the contact force and hip moment:

Definition of a fixed contact zone: the pressure map used in the DHErgo experiment was a 48x48 grid of sensors. As a consequence, it was considered that the contact zone of the left hip should be limited to the bottom left quarter of the grid (row 25 to 48, column 25 to 48) as illustrated in Figure 17.

Estimation of the contact force: it was considered that all the pressure map's sensors in contact with the thigh had the same orientation. As a result, F Thigh-Seat was calculated as:

With i, a grid row, j, a grid column, P i,j , pressure recorded by the sensor (i,j) on the grid (in PSI, i.e. pound squared inch) S i,j , surface of the sensor (i,j) in squared inch , conversion parameter from pound to newton Estimation of the resulting hip moment: it was considered that the contact force was perpendicular to the thigh in the XZ plane of the mock-up reference frame. Besides, the force was applied at 1/2 of the thigh length from the hip joint. For each subject, the thigh length were based on the length of the thigh segment of the individualized RAMSIS™ manikin. As a result, T Thigh-Seat was calculated as:

Table 4 presents the estimation of F Thigh-Seat and T Thigh-Seat for the pedal configuration tested in the DHErgo experiment. Globally, it can be noticed that the contact force between the left hip and the seat was 104 N on average and the resulting moment was 22 Nm on average. The contact force does not appear to be negligible. As a consequence, only the knee and ankle joint toques calculated by inverse dynamic reconstruction should be considered as reliable.

3A n a l y s i s of the discomfort perception

The discomfort perceived during the clutch pedal operation experiment performed in DHErgo project was assessed using a questionnaire (see in Appendix). The objective of such questionnaire was to collect a subjective global evaluation of the task using a discomfort rating scale, but also to get some insights on the effect of the pedal configuration parameters (seat, pedal positions at the start/end depression, pedal travel length/angle, pedal resistance) on the discomfort. Besides in the context of using the less-constrained movement concept, the analysis of the answers should identify the pedal adjustments made by the subjects as well as explain the effect of these adjustments on the perception of the task. In the following section, the answers to the questionnaire were analyzed. The answers were analyzed in terms of the following three independent variables:

The group of subjects (SubjectGroup): young male, young female, old male and old female. Each group is constituted of 5 individuals of same age, gender and anthropometry.

The configuration (Configuration): BMW1, BMW2, BMW4, PCA1, PCA2 and REN3.

The type of configuration (ConfigType): imposed configuration and less constraint one.

For each question, answers were considered. First, the multiple-choice questions Q1 to Q6 were considered and then discomfort ratings were analyzed.

Questionnaire analysis

The multiple-choice questions focused on the subjective evaluation of the clutch pedal configuration and the effect of the seat on discomfort perception during the task:

Question 1 (Q1) evaluated the effect of the seat on discomfort.

Question 2 and 3 evaluated respectively the clutch pedal position at the beginning of the pedal depression and at the end:

o The height of the pedal (Q2H/Q3H), i.e. the z-coordinate in the mock-up local coordinate system, o The distance from the seat (Q2D/Q3D) i.e. the x-coordinate, o The lateral position (Q2P/Q3P) i.e. the y-coordinate.

Question 4 (Q4) evaluated the travel length.

Question 5 (Q5) evaluated the travel inclination.

Question 6 (Q6) evaluated the perception of the pedal resistance.

The effects of the three variables (Group of subjects, Configuration, Type of configuration)

were analyzed using frequency tables. The Chi-square test was also performed to test the hypothesis that the row and column distributions are independent. This analysis focused particularly on the answers to the questions 2, 3 and 6. But the influence of the seat (Q1), the travel length (Q4) and travel inclination (Q5) were also assessed by the subjects. For more than 90% of the answers, the seat had no effect on the discomfort perception. The travel lengths of the clutch pedal were estimated too long or at good length at almost fifty-fifty. The travel inclinations of the clutch pedal were mostly estimated (72%) at good inclination. All results are presented in Appendix. Prior to the analysis, the reproducibility of the answers to the questionnaire was assessed in order to verify if the subjects were consistent in their evaluation of the pedal configuration.

3.1.i Reproducibility

For each subject, the central configuration PCA2 was tested three times for both imposed and less constraint types of configuration.

The questions Q1 to Q6 were multiple choices questions. In order to assess the reproducibility of each subject for these questions, answers were attributed to each question according to the number of similar answers on the three repetitions. The reproducibility was defined for each subject and question as the frequency of each condition (Figure 18). The results are presented in Appendix. Globally, the subjects were able to give at least 2 similar answers to the same question in 98% of the cases. The same answers for the three repetitions was obtained in 61% of the cases. More specifically, the most reproducible question was the question on the seat Q1 (90% of Cond3) and the worst were the as good or too far in respectively 57% and 38% of the answers in final position. The clutch pedal was estimated at a good lateral position in most of the answers, respectively 64% and 67 % for initial and final position. The rest of the answers, respectively 36% and 32 % for initial and final position, considered the pedal as too on the right. More important is the effects of the type of configuration on the answers. Big differences were observed for the initial pedal position. More than 90% of the less constrained pedal configurations were considered "at good height, at good distance and at good lateral position" in the initial position. Imposed pedal configurations in the initial position were found too high in 55% of the answers to Q2, too close in 40% and too on the right in 64%. In the final position, the effects of the type of configuration were also significant but apart from lateral position evaluation, they were less strong. Indeed, the pedal were found much more frequently too low and too far in less constraint configurations compared to imposed ones: respectively 22% and 16% of the answers to Q3 for pedal height, 42% and 32% for pedal distance. Therefore the subjects tended to improve the beginning of the clutch pedal operation at the expense of the end. Figure 19 compares the distributions of the responses to Q2H, Q2P, Q3H and Q3P between freely adjusted and imposed pedal configurations. However, although a significant effect of subject group on discomfort scores was observed, the average difference between subject groups were between 2.3 and 6.1, smaller than the average maximum gap of three repetitions, i.e. 6.8. In what follows, the analysis is therefore focused on the effects of pedal parameters, i.e. the configuration and the type of configuration, on discomfort ratings. According to Wang et al. (2004), one way to reduce subject effect was to centre the rating scores for a configuration at the average score of all configurations tested for each subject. Therefore centred rating scores rather than direct ones were used for studying the effects of pedal parameters on discomfort. The less constrained configurations were better rated than the imposed ones (Figure 22). Then, pedal configuration had a strong effect on the discomfort ratings (Figure 23). BMW1

Condition

had the lowest discomfort score with a mean centered value of -3.78. PCA1 and REN3 had the highest discomfort ratings respectively of 3.33 and 4.15. BMW1, BMW2, PCA2 had average scores between 0.48 and 1.21.

3.2.iii Discomfort/pedal resistance perception relation

In an earlier study by Wang et al. (2004), it was found that pedal resistance had a strong effect on discomfort perception. In this study, pedal resistance was not controlled but rather dependent on its initial position and travel length. The perception of the pedal resistance was collected through the questionnaire using the Borg's CR10. A strong effect of the perceived pedal resistance on the discomfort ratings was found. Figure 24 showed that discomfort ratings increased with the perceived level of pedal resistance.

4M o t i o n analysis of the clutch pedal operation

In this part, clutching movements were analyzed in order to understand the subjective evaluations made by the subjects. First, the motion kinematics was analyzed comparing the imposed and less-constrained configurations. Indeed, the analysis of subjective evaluations showed that less-constrained configurations led to an improvement of the clutch pedal position at the start depression at the expense of the end. But free pedal adjustment had no significant effects on pedal force perception. This kinematic analysis focused on the clutch pedal adjustments and their effects on joint angles to explain why the subjects preferred to improve the start depression than the end.

Second, the motion dynamics was analyzed focusing on the foot force applied on the pedal.

The pedal resistance depended strongly of the configuration and could be considered a dominant factor for discomfort assessment in the analysis of discomfort ratings. Therefore the foot force applied to the pedal (amplitude and direction) was studied as well as its effect on joint torque.

Comparison of clutching movements for imposed and less constrained pedal configurations

4.1.i Clutch pedal position adjustments

The variation in pedal position between imposed and less-constrained configurations was estimated using the six markers fixed on the clutch pedal. For each pedal configuration, the positions of the clutch pedal markers of both imposed and less-constrained clutch pedal operation were collected at the first frame, i.e. before the start depression. The reproducibility of the adjustment in each axis (x-, y-and z-axis) was defined as the maximum gap between adjustment scores and their mean for each subject and was estimated from the three repetitions of the central configuration PCA2 in the experiment. The mean values were 15 ± 9 mm in x-axis, 10 ± 8 mm in y-axis and 13 ± 9 mm in z-axis.

The pedal position change in x, y and z directions from imposed pedal positions to preferred ones were summarized in Table 9. On average, they were -8.8, -21 and -16.8 mm respectively in x, y and z directions. Globally the pedals were moved further away from the seat, lower and more leftward. Significant effects of the group of subjects were found for the adjustment on x-axis and zaxis. For the adjustment in the longitudinal direction (x-axis), the younger subjects put the pedal further from the seat than the older people. In the vertical direction (z-axis), only the adjustments of the younger males differed from the other subjects. Globally, all subjects tended to adjust the pedal in the same direction, i.e. further from the seat, lower and more on the left.

A significant effect of pedal configuration was observed, particularly strong for the adjustment in y-axis and z-axis. In y-axis, apart from BMW2 and BMW4, the pedals were moved more than 15 mm on left on average. The average lateral position of the less constrained configurations was therefore -109 mm from the central line of the seat. In z-axis, BMW1, BMW2 and BMW4 were lowered more than 40 mm on average whereas PCA1, PCA2 and REN3 presented little displacements.

4.1.ii Joint angle analysis

The left lower limb had seven degrees of freedom (DOFs): three at the hip (flexion/extension, abduction/adduction and axial rotation), two at the knee (flexion/extension, axial rotation) and two at the ankle (flexion/extension or dorsiflexion/plantarflexion, inversion/eversion). The orientations of the joint axes from RAMSIS™ were kept to express the joint angles:

GHUL-x, -y and -z respectively for hip axial rotation, abduction/adduction and flexion/extension GKNL-x and -y respectively for knee axial rotation, and flexion/extension

GSPL-x and -z respectively for ankle inversion/eversion and inversion/eversion

The joint angles were examined at the beginning and the end of clutching movement. Paired ttests were performed to estimate whether joint angles differed between imposed and less- The resultant pedal force varied from 166N to 184N on average depending on pedal configuration. A strong effect of the pedal configuration on the pedal force resultant and its components was found. BMW4 had the lowest force resultant, whereas REN3 had the highest force resultant. The same observation can be made for the tangential component, which is the only component useful for moving the pedal. However, no significant effect of the type of configuration was observed on pedal force. Between imposed and less constrained configurations, the difference in the force resultant was only 4 N on average.

A strong effect of the group was also found, especially on the tangential and normal components. Indeed, the mean values of the normal and tangential components were respectively higher for younger subjects and older ones. Though older and younger subjects had quite similar the tangential force -displacement curves, the older participants tended to apply more force at the end of the depression (Figure 27). Moreover, the verticality of the curves of older subjects at the end of travel may suggest that the older subjects continued to increase the force applied on the pedal whereas it reached at its travel's end. For the normal component, the patterns of the curves are also quite similar between older and younger subjects (Figure 28). But the younger subjects had on average a higher the normal force component than the older subjects except for BMW2. The normal component increased quickly during the last third of the clutch pedal travel. 11). As for the force resultant and components, it was affected by pedal configuration and group of subjects. About the pedal configurations, BMW4

had the force direction with the largest angle compared to Hip-P App axis with -9° for mean value and REN3 had the narrowest angle with -1.6° for mean value. It can also be observed that BMW1, BMW2 and PCA2 had close mean values of , i.e. respectively -5.2°, -5.5° and 6.8°. About the group of subjects, it can be noted that the older female had a mean value of superior to 0. Globally, the younger subjects had largest mean angle than the older subjects, i.e. -6.1° and -11.4° for younger females and males versus 0.8° and -2.70 for older ones. (deg About the lateral deviation, the configuration and the type of configuration had strong effects on and . The effect of the group of subjects was found weak. The Hip-P App axis with a mean lateral deviation of -0.2° was close to be parallel to the x-axis and slightly directed rightward in the driver's point-of-view. On the contrary, the pedal force with a mean value of of 1.6° was directed leftward on average. Interestingly, it can be noticed that the gap between the pedal force direction and the x-axis increased significantly with the lessconstrained (from 0.6° to 2.5° on average) and that the lateral deviation of the Hip-P App axis changed from a rightward direction for imposed configurations (-0.7° on average) to a leftward direction for less-constrained configurations (0.2° on average). Finally, about the pedal configuration, the average values of showed that the Hip-P App axis was oriented leftward for BMW2 and BMW4, respectively 0.4° and 0.5°, whereas it was oriented rightward for the other pedal configurations.

) (deg) (deg) Older Female 0.8 ± 8 2.4 ± 2.6 -0.1 ± 1.3 Older Male -2.7 ± 6.3 1 ± 2.8 0.1 ± 1.5 Younger Female -6.1 ± 8.1 1.7 ± 2.9 -0.3 ± 1.2 Younger Male -11.4 ± 8.5 1.6 ± 3.9 -0.6 ± 1.1 BMW1 -5.2 ± 9.1 0 ± 3.1 -1.3 ± 1.5 BMW2 -5.5 ± 6.7 2.1 ± 2.3 0.4 ± 0.9 BMW4 -9 ± 7 1.6 ± 2.3 0.5 ± 0.9 PCA1 -3 ± 10.1 4.2 ± 4.5 -0.3 ± 1.3 PCA2 -6.8 ± 9.2 0.8 ± 2.6 -0.2 ± 1.1 REN3 -1.6 ± 7.3 2.5 ± 2.6 -0.7 ± 1.4 Imposed -6.1 ± 8.4 0.6 ± 3.3 -0.7 ± 1.3 Less Constrained -5.1 ± 9.2 2.5 ± 2.

4.2.iii Joint torque analysis

Joint torques were projected onto the rotation axes of their respective joint. As for the inverse kinematics method, the orientations of the joint axes used were from RAMSIS™ kinematic model (see in Appendix). The joint torques were analyzed only at the end of pedal depression.

As the contact forces between the thigh and the seat were not estimated, the hip joint torques were not considered. Only the joint torques on the flexion/extension axes were investigated because the clutch pedal operation was considered as a mainly flexion/extension operation.

Knee and ankle flexion/extension torques were significantly affected by pedal configuration and group of subjects but not by configuration type (Table 12). The other configurations have quite the same mean value of moment, i.e. between -12 and -13

Nm.

As for the foot force on pedal, a strong effect of the group of subjects was found. Old subjects had higher knee torques than the young ones whereas for ankle, it was female subjects that had lower joint torques than the males.

Discussion

The main conclusions of the discomfort perception and motion analysis of the clutch pedal operation can be summarized as:

Lower discomfort ratings were obtained for less constrained configurations, A clutch pedal position further away from the seat, lower and more on the left, from driver's point of view, was usually preferred, Freely adjusted pedal position improved the beginning of the clutch pedal operation at the expense of the end, Free pedal position adjustment led to a decrease of the flexion of the lower limb joints at the beginning and an increase of the extension of these joints at the end of the pedal depression, especially for the ankle,

The pedal resistance perception was highly dependent on pedal configuration and correlated with discomfort ratings.

The foot force on pedal and the joint torques were highly dependent on the pedal configuration,

The group of subjects had significant effects on the foot force on pedal and on the knee torques.

The analysis of the discomfort ratings showed that discomfort was decreased by adjusting the position of the clutch pedal. However, the results should be interpreted with caution. Indeed the average difference in raw CP50 scores between imposed and less constrained configurations was 5.1, whereas the reproducibility in rating score was 6.8.

Pedal position adjustment and discomfort perception

The analysis of the clutch pedal position adjustments showed on average that the subjects moved the pedal further away from the seat and also lowered it for the less-constrained configurations. The pedal adjustment led to a decrease in the flexion of the hip, knee and ankle joints at the beginning and an increase of the extension of these joints at the end of the pedal depression as the travel length was kept unchanged. The difference in the joint flexion extension angle between freely adjusted and imposed configurations was the highest for the ankle. Compared to the joint ROM values collected, the ankle angle was very close to its dorsiflexion limit at the beginning of pedal depression (Table 13) for the imposed configurations. The subjects might adjust the pedal position so as to reduce the ankle dorsiflexion angle away from its limit when depressing the pedal. This may explain the decrease in discomfort rating for preferred pedal configurations. Hip abduction was also increased at the beginning and the end of pedal depression, which is related to lateral adjustment of pedal position. There may be two explanations. It could be explained by the minimisation of the distance from rest position to the pedal at the beginning of the depression, according to the hypothesis of minimum work suggested by Wang et al. (2004). Another explanation could be that the participants may have preferred a pedal aligned with the hip in the plane passing through left hip centre and parallel to sagittal plane. Indeed, it was found that the transversal component of the pedal force was oriented leftward in driver's point of view in the plane XY of the mock-up reference frame.

Therefore, it could suggest that the pedal lateral adjustments were made in order to align the pedal force direction and the Hip-P App axis in the XY plane. However the data suggested that the gap between pedal force direction and the Hip-P App axis in the XY plane increased with the pedal adjustments, from 1.3° to 2.3°. Besides it was found that the absolute value of the transversal component increased on average from imposed to less-constrained configuration.

But the data also showed that the orientation of the Hip-P App axis changed from a rightward direction for imposed configurations to a leftward direction for less-constrained configurations. As a consequence, the lateral pedal adjustments may due to a preference of the subjects to have a leftward deviation of the pedal force.

Pedal force control and discomfort perception

It was observed that pedal configuration had strong effects on clutch pedal force (resultant, components and direction) and joint torques (knee and ankle). From the analysis of the discomfort perception, it was found pedal configuration had also strong effects on pedal resistance perception and discomfort ratings. Besides, a strong effect of the perceived pedal resistance on the discomfort ratings was found. Therefore the correlations between clutch pedal tangential and normal forces, pedal force direction, joint torques (knee and ankle) and the CP50 raw scores were investigated (Table 14). It can be observed that only the joint torques were significantly correlated with the discomfort ratings but the correlation was also weak. This observation is in agreement with Wang et al.

(2004) who had also found correlation between discomfort ratings and joint torques, especially knee torques. Interestingly, the correlations of knee and ankle torques with the CP50 scores were opposite. This was expected because geometrically, in order to decrease the pedal force lever arm on the knee joint, the one on the ankle joint need to be increased. This explains also the negative correlation coefficient between knee and ankle torque. Moreover, it can be noted that considering the correlation coefficient, the knee torque decreased when the discomfort ratings increased. Because knee torques were negative, it means that higher discomfort was perceived with higher knee torque in absolute. Indeed, BMW4 configuration was the least uncomfortable pedal configuration according to discomfort questionnaire and also had the lowest mean knee torque value, i.e. -12 Nm; whereas PCA1 and REN3 were the most uncomfortable pedal configurations and had the highest mean knee torques, i.e.

respectively -25.4 Nm and -27.4 Nm. On the contrary, considering that ankle torques were also negative, higher perceived discomfort were correlated with lower ankle torques.

As expected, the knee torques were also significantly correlated with tangential and normal components of pedal force as well as the pedal force direction. Yet the correlation was especially strong with normal pedal force and pedal force direction. The strong correlation, 0.83, between pedal force direction and normal pedal force suggested that the pedal force direction and so the knee torque were controlled by the normal component of the pedal, which privileged than the comfort at the end of travel. Moreover, the results also showed, in agreement with Wang et al (2004) that discomfort rating was strongly dependent on pedal resistance and that discomfort was significantly correlated to joint torques at the end of pedal depression, especially to the knee joint torque.

Taking all these observations into account, this section of the study aimed at using kinematic and dynamic parameters of leg depression movements for building discomfort indicators.

Kinematic indicators are based on leg position during the task. Dynamic indicators represent the joint loads due to external force exertion.

5.3.i Discomfort indicators for clutch pedal operation

For the clutching task investigated in the current study, kinematic parameters could be three Among the proposed indicators, IndHipEnd, IndKneeEnd, IndAnkleStart and IndKneeJT were significantly correlated with raw CP50 scores, and IndLPos with the centred CP50 scores (Table 15). IndHipEnd, IndKneeEnd, IndAnkleStart and IndKneeJT as the raw CP50

scores depend on the subject's characteristics (anthropometry, physical capacity, perception …), whereas IndLPos and centred CP50 scores do not. This may explain the correlation with either the raw or the centred CP50 scores. Note the negative correlations with IndHipEnd and IndKneeEnd. This is expected as the less-constrained configurations caused an increase in lower limb joint extension at the end of depression, and thus an increase in discomfort according to the cost functions. IndHipEnd, IndKneeEnd, IndAnkleStart and IndKneeJT are significantly correlated each other.

Limitations

There are several limitations of the present approach that can be pointed out.

Firstly, defining objective discomfort indicators from biomechanical parameters is very challenging. Many biomechanical parameters can be considered. In this study, relevant parameters were selected by comparing imposed and less-constrained configurations. As only the pedal position could be adjusted for less-constrained configurations, the proposed discomfort indicators may not be applicable to other situations where other pedal design parameters are adjustable. However, we believe that the proposed approach could be used for identifying relevant discomfort criteria. Thirdly, the proposed discomfort indicators are based on kinematic and dynamic parameters of a movement under investigation, implying that motion simulation should be experimentally validated. The definition of dynamic and kinematic indicators is quite generic. However, the definition of the dynamic indicators could be an issue. Indeed, their definition is based on the knowledge of maximum isometric joint torque and these types of data are hard to get. Some data exist in the literature but the variability of joint strength according to age, gender, anthropometry or posture is unknown. Currently, the most reliable way to get these data is experimentally. Therefore, the integration of such discomfort modelling in DHM is difficult.

Finally, by definition, clutch pedal depression is not a very uncomfortable task. Discomfort would be caused rather by the long repetition of clutching movements, in a traffic jam or during an urban journey. Under the current experimental conditions, subjects were asked to assess a pedal configuration without any real task associated. Only the discomfort related to the clutching movement was investigated, not the task associated with the movement. More realistic experimental conditions should be encouraged in future.

7C o n c l u s i o n

By comparing six existing pedal configurations and corresponding freely adjusted ones, results showed that pedal position was adjusted in all three directions to ensure a good starting pedal position at expense of its end position. Low joint torque at the end of depression was associated with the tested pedal configurations with a low discomfort rating, confirming earlier findings by Wang et al (2004) that the pedal resistance is a dominant factor in discomfort evaluation of the automotive clutch pedal. By comparing the movements of the imposed and freely adjusted pedal configurations, relevant biomechanical parameters were identified for proposing 9 discomfort indicators.

The present work illustrates that the less-constrained motion concept was useful for identifying motion-related biomechanical discomfort indicators. The method presented in this study lies within the scope of industrial needs for product design assessment. The proposed discomfort indicators provide objective information helpful for design engineers to compare alternative designs.

Finally, the study confirmed the importance to take into account the dynamic parameters (joint torques, muscle forces …) of a motion for discomfort assessment. the proposed discomfort indicators are based on kinematic and dynamic parameters of a movement under investigation, implying that motion simulation should take into account kinematic and dynamic constraints and should also be experimentally validated when using a DHM..

Appendix

Selection of the test clutch pedal configurations

A principal components analysis is done to define a plan of comparison of the different configurations in order to select a space of experimentation. For this case study of DHErgo project, end-users provided 11 clutch pedal configurations: 5 by BMW (BMW1 to BMW5), 3 by PSA Peugeot Citroën (PCA1 to PCA2) and 3 by Renault (REN1 to REN3) (Table 16).

According to the study of [START_REF] Wang | Experimental investigation and biomechanical analysis of lower limb movements for clutch pedal operation[END_REF] First the correlation matrix of the design parameters was calculated. The R coefficient of Bravais Pearson was used (Table 17). This coefficient considers two parameters as correlated if it value is superior to 0.6.

MK L MK L MK 7 t K Ju K L K L K L K L K T K T K L K Sh K Sh K L K L MK L K L MK L MK R

Indiquez l'intensité de la gêne ressentie selon l'échelle suivante

Imperceptible

Très

8.15.i CP50 linear regression

The Table 27 presents the coefficients of the indicators in the fitted model with their respective p-values, the coefficients of determination R² and adjusted R². By definition, the R² measures the percentage of the variability in Y, i.e. the dependent variable that has been explained by the fitted model. Adjusted R² measures also this percentage of variability but compensates for the number of variables in the model. A regression model selection procedure was also performed to evaluate the behaviour of the fitted model according to the number of indicators considered. The Table 28 presents the best n-variable models with n from 0 to 9 according to adjusted R² and their classification. It showed that the best model in terms of adjusted R² was a 7 variables-model and the best compromise was a 4 variables-model. Between these two models, the adjusted R² improved of only 1%. None of these two models included the IndKneeJT whereas it was found to be the more significant to differentiate clutch pedal configuration. Besides, this indicator is included in only one model, the one including all the indicators.

8.15.ii Linear discriminant analysis model

Three distinct discomfort categories of the modified CP50 rating scale used in the experiment were considered: imperceptible (0 on CP50), low (11 to 20) and high (31 to 40). The categories were composed of 16, 97 and 17 trials respectively. In order to maximize the robustness of the model, sets of 15 trials were randomly selected in each category. The general idea was to perform the discriminant analysis with trial samples of the same size in each category. One of the issues was the limited data available in the two extreme categories.

It was decided to select almost all available trials in these two categories. As a consequence, the cross-validation of the model was mostly performed on the unselected data of the "low" category.

For each selected trials, all discomfort indicators were considered. Then a linear discriminant analysis (LDA) was performed using XLSTAT software. This method is used to find a linear combination of parameters which characterizes or separates two or more classes of variables.

Compared to regression analysis, the LDA considers categorical dependent variables and not numerical quantitative dependent variable. In this case, the LDA provided three linear discriminant functions, one for each category. The Table 29 and Table 30 presented the three linear discriminant functions of each model: F1, F2 and F3 corresponding respectively to imperceptible, low and high discomfort categories. Then the principle is to calculate the results of each function with the indicators of a trial. The function which gives the higher score determines the category in which the trial belongs. The Table 31 showed the percentage of right prediction for the data selected to build each model. The cross-validation was performed using the unselected trials of the three considered discomfort categories.

Introduction

Accurate representation of task-oriented postures is critical when digital human models are used for ergonomic assessments. For force exertion tasks such as automotive control tasks, the posture is not only adapted to geometrical restrictions but also to the force requirements of the task. Besides, posture has a major influence on strength capability as it determines the mechanical advantage offered to muscles to exert force (Haslegrave, 2004).

Force exertion capacity can vary greatly depending on a high number of factors [START_REF] Daams | Isometric Pull-Push Strengths in workspace: 1 Strength Profiles[END_REF][START_REF] Kumar | Muscle strength[END_REF]Haslegrave, 2004). In case of automotive pedals, some studies investigated lower limb maximum strength [START_REF] Kroemer | Foot operation of controls[END_REF][START_REF] Mortimer | Brake force requirement study: driver-vehicle braking performance as a function of brake system design variables[END_REF][START_REF] Pheasant | Human strength in the operation of tractor pedals[END_REF]Metha et al, 2007) but few focused on the force perception [START_REF] Wang | Pedal operation[END_REF].

The experimental conditions varied a lot from a study to another on for example the type of pedals (accelerator, brake or clutch pedal), the range of end effector positions (align or not with hip joint center) or the type of vehicles. Besides, postures of force exertion are usually only succinctly described. Only a few studies in the literature investigated the mechanism of the control of the force applied on a pedal [START_REF] Wang | Experimental investigation and biomechanical analysis of lower limb movements for clutch pedal operation[END_REF][START_REF] Schmidt | Foot force direction in an isometric pushing task: prediction by kinematic and musculoskeletal models[END_REF]. Both studies suggested that the control of the foot force may follow the principle of reducing joint load, but were performed in 2D whereas, as a pedal may not be fully aligned with the left or right hip joint, the force direction should be 3D, especially for high force exertion level.

Most of the existing DHM packages used in the automotive industry for ergonomic assessment of a product or workplace have a kinematic approach of the human motion without considering the dynamics. The simulation of force exertion postures usually requires manual manipulation of the manikin and so some expertise. More recently, some optimization-based methods were suggested [START_REF] Seitz | FOCOPP -An approach for a human posture prediction model using internal/external forces and discomfort[END_REF][START_REF] References | Physic-Based Digital Human Modeling: Predictive Dynamics[END_REF]. These simulation methods have the advantage to be able to explain postural strategy, as they consider motion control strategies as optimization criteria to predict the posture. But they have not been validated.

The data used in this study were collected in a collaborative project between IFSTTAR and the car manufacturer Renault (called 'FAC project' afterwards) on the muscular capacity of the upper and lower limb when operating on automotive controls. The aim of this collaborative project was to collect data on the force exertion capacity of the upper and lower limbs to define ergonomic criteria for the improvement of the ergonomics of automotive controls. Only the part on the clutch pedal was investigated and presented. A complete presentation of the whole experimental protocol and the preliminary results can be found in the report by [START_REF] Wang | Capacités musculaires des membres inférieurs et supérieurs appliquées aux commandes automobiles et leur perception d'effort[END_REF].

The purpose of the study presented in this section was to better understand the mechanisms of the pedal force exertion when changing the level of force exertion and pedal position. In particular, the force and posture control strategies were investigated in order to propose improvements of DHMs in automotive control tasks simulation. The study was organized in three parts. First maximum pedal force exertion on different pedal positions were investigated. Then the effects of pedal force level on force perception, posture and force direction were analyzed. Finally, optimization-based methods were used to explain both force and posture controls during pedal force exertion. The results from simulations were compared with experimental observations.

Material and methods

Subjects

Thirty voluntary male and female subjects participated in the experiment (Table 32). They The experimental protocol was approved by the ethical committee of Ifsttar. Informed consent was given before participating in the experiment.

Twenty one anthropometric dimensions were measured for all participants (see in Appendix).

Experimental set up

2.2.i Mock up

A multi-adjustable experimental vehicule package was used to define different driving configurations (Figure 40). It was composed of a seat, a steering wheel, a floor, a clutch pedal, a gear stick and one hand brake. The location of all of these elements could be adjusted according to the configurations to be tested. They were defined in a specific mock-up coordinate system centered at a reference seat H-point located with help of SAE H-point machine (SAE J826) and a FARO arm digitizer. The x-axis was directed backwards, y-axis laterally to the right and z-axis upward. The seat slide angle was fixed at 4°. Then for each pedal configuration, the gear stick was fixed at an average position ([-307 mm; 337 mm; 145 mm] in the mock-up coordinate system). The hand brake was not used for the experiment on clutch pedal.

2.2.ii Motion capture

The same motion capture system as the one for DHErgo experiment was used (VICON system with ten MX T40 cameras sampling at 100 Hz). Fourty two reflective markers were put on the subject. Thirty-five markers were placed on the multi-adjustable mock-up to characterize the geometry, the position of the car elements (seat, steering wheel, clutch pedal, floor …) and the orientation of the force sensors. The locations of the markers described in Appendix.

2.2.iii Force measurements

About the force sensors, the mock-up was equipped with the same pedal force sensor as the one used for the DHErgo experiment to measure the force exertion on the clutch pedal, i.e. a TME 3-axis force sensor with a capacity of 1500 N on each axis. Three other force sensors were also used during force exertion on the pedal:

A BERTEC force plate on the floor (Figure 41a)

A gear stick handle shaped TME 3-axis force sensor with a capacity of 1000 N on each axis (Figure 41b)

A 6-axis force sensor (Denton 2554) between the steering wheel and the steering column (Figure 41c) The experiment on the clutch pedal was divided in two parts.

Maximum force exertion on static clutch pedal (called "ExpMax" in the rest of this study).

In this part of the experiment, the six configurations were tested randomly. For each configuration, a rest trial and then a maximum trial were performed.

Force perception on static clutch pedal (called "ExpPcp" in the rest of this study).

For this part of the experiment, only one configuration was tested (P2F) depending on five modalities of force level: very low, low, medium, high, and maximum. The levels were chosen considering the Borg's CR-10 [START_REF] Borg | Borg's perceived exertion and pain scales[END_REF]. A rest posture was also measured prior to force perception trials. Each subject performed first two maximum force exertions. A third one was measured if the relative gap between the two trials exceeded 10% of the maximum. Then the four other force level exertions were randomized.

As a result, each subject performed 6x2=12 trials in ExpMax and 6 (+1 if necessary) trials in ExpPcp, i.e. 18 (+1) trials in total.

Experimental procedure

The experiment began with the measurement of the main subject anthropometric dimensions and the placement of the reflective markers. Photos from two orthogonal views synchronized with a VICON motion capture were recorded on a calibrated throne.

Prior to experimentation, each subject was asked to adjust the seat position longitudinally in a standard driving configuration with respect to the clutch pedal end position corresponding to the position P2F and to the steering wheel but with a seat height fixed at 286 mm. The seat adjustment should be in a specific range defined by Renault. The formula provided gives the position of the seat relative to the reference H-point according to the length of the lower limbs, with a confidence interval of 80%.

With , the length of the lower limb (in mm) L Thigh , buttock-knee length (see anthropometric measurements in Appendix)

h Knee , knee height sitting (see anthropometric measurements in Appendix)

, distance from the reference H-point (H ref ) to the clutch pedal position at end-travel (CP end ).

Based on the recommendation for measuring muscle strength by [START_REF] Gallagher | Physical strength assessment in ergonomics[END_REF], the subjects were instructed to exert their maximum force (or an intermediate force level) as fast as possible once a red light turning on and to maintain the maximum force until to the light turning off. The force exertion duration was 5 seconds for each trial. Non-verbal encouragement was given during the force exertion. At least two trials were repeated for maximum force measurement. If more than 10% difference between the two repetitions, third measurement was performed. The trial with maximum force level was retained. At least two minutes of rest were imposed between two maximum force trials. For intermediate force levels, at least 40 seconds were proposed. For the left foot pedal operation, the subject was asked to place the left hand on the top left of the steering wheel, the right hand on the gear stick handle, the right foot on the floor and the left foot on the clutch pedal. A special caution was taken on the left heel which should not be in contact with the floor during pedal force exertion. The whole duration of the experiment lasted about four hours and a half. But this duration was for the whole experiment including maximum force exertion and force perception of handbrake and gear stick. The clutch pedal part represented in terms of trials about 20% of the whole experiment.

Data processing

From the FAC project, following data types were available:

Anthropometric measurements for each participant 3D markers trajectories attached on both subjects and mock-up Force exertion measurements on the force plate (right foot), the gear stick sensor (right arm), the steering wheel sensor (left arm) and the pedal sensor (left foot).

As this study focused on the pedal force exertion, only the force data from the pedal sensor were considered.

The global workflow of the data processing for inverse kinematic and inverse dynamic motion reconstruction was the same as the one used on the DHErgo experiment data. Please refer to the "data processing" section of the DHErgo experiment for more details. Only the validation of the motion reconstruction and the treatment of the pedal force specific to the FAC project are described in the following parts.

2.5.i Pedal force treatment (ExpMax and ExpPcp)

In the FAC project, the static pedal force was determined using the plateau method. The instruction to participants was to apply a required level of pedal force as rapidly as possible and to maintain it for 5 seconds. The mean value of each trial was calculated from 1.5s to 4.5s. This range was chosen to avoid the loading phase at the beginning of the force exertion and the slackness at the end. An exclusion threshold was defined on the value of the standard deviation of each trial. It was fixed at 70N, which represented 8 trials, i.e. 1.5% of the trials.

2.5.ii Pedal force perception (ExpPcp only)

In the ExpPcp of the FAC project, the force levels were chosen considering the Borg's CR-10 [START_REF] Borg | Borg's perceived exertion and pain scales[END_REF]. This scale is a category (C) ratio (R) scale in 10 points developed for studying perceived exertion. A power law between the perception level, i.e. the category, and the stimuli, i.e. the magnitude of pedal force, was implicitly assumed. The CR10 also assigns a scalar, i.e. the ratio, to each force level verbal term, which linearizes the power law. Table 33 shows the force level used in the ExpPcp with the corresponding scalar. In this study, the linear relation was considered to analyze the pedal force perception law. In order to compare different subjects, the pedal force resultants of each level were also normalized for each subject by his/her maximum force. These normalized pedal forces were used to analyze the pedal force perception of the ExpPcp.

(

2.6

Evaluation of reconstructed motions

As for DHErgo experiment, the forces applied by the participants on the seat were not recorded. Moreover, in this experiment, there were no pressure maps to indicate a raw estimation of the contact force between the left thigh and the seat. That's why, only the quality of the kinematic motion reconstruction was assessed. First, the normal and tangential forces are the main force components. On average, normal, tangential and transversal components were respectively -237 N, 610 N and -60 N. Significant effects of both the group of subjects and the pedal position were found on the force resultant and the three force components. Regarding the effects of pedal position, a lower force resultant was found at the end-travel than at mid travel (Figure 45a). This difference between the end-travel and mid-travel positions can also be observed for tangential and transversal forces. However, the normal components (absolute values) were higher at the end than at midtravel (Figure 45b), i.e. from -279 N to -284 N for the end-travel positions versus from -176 N to -218 N for the mid-travel positions.

As expected, short women had a force capacity significantly lower than the 2 male groups (Figure 45c). The maximum force resultants were 421 N, 764 N and 772 N on average respectively for short women, average men and tall men. Short women had also significantly lower maximum forces in all three components than the men. Two male groups had almost same force exertion capacity.

3.1.ii Pedal force direction

As for the DHErgo experiment, the pedal force direction was investigated in two ways. First, the direction of the pedal force was analyzed in the XZ plane with respect to the hip-force application point axis. Second, the effect of the lateral component on the pedal force direction was investigated in the plane XY. , the pedal force direction in the plane XZ, and , the lateral deviation of respectively the pedal force direction and the Hip-P App axis in the plane XY were defined as for the DHErgo experiment. The results are presented in Table 35. -10.8 ± 6.4 5.5 ± 1.7 -1.5 ± 0.9 P2F -5.9 ± 6.8 5.4 ± 2 -1.3 ± 1.1 P2M -11.1 ± 5.4 5.9 ± 1.9 -1.7 ± 0.9 P3F -5.9 ± 6.4 5 ± 2.6 -1.6 ± 1.1 P3M -10.8 ± 5.3 6.9 ± 2.2 -1.7 ± 1.1 Short women -5.9 ± 7.2 6.6 ± 1.5 -2.2 ± 1.2 Average men -7.4 ± 5.9 6 ± 1.7 -1 ± 0.9 Tall men -11 ± 6.1 4.4 ± 2.3 -1.4 ± 0.7 Total -8.4 ± 6.7 G***, P*** 5.6 ± 2.1 G***, P*** -1.5 ± 1 G*** *p<0.05, **p<0.01, *** p<0.001. G: group of subjects; P: pedal position had an average value of -8.4°. Significant effects of both pedal position and subject group were found. A post-hoc test showed two homogenous groups for the pedal positions:

the mid-travel positions (P1M, P2M, and P3M) with an average angle around -11°, the endtravel positions (P1F, P2F and P3F) with an average angle around -6°. Short women and average men had similar mean values of , respectively -5.9° and -7.4°, whereas the tall men had larger average angle, around -11°.

About the transversal deviation of the pedal force, the main observation is that the subjects did not exert force in the same direction as the Hip-P App axis in the XY plane. Indeed, on average, and were 5.6° and -1.5°. The hip-force application point axis was oriented slightly rightward whereas the pedal force was oriented leftward (from the driver's point of view). There was significant effect of the group of subjects on and .

But the maximum differences between the groups were small. They were respectively 2.2° between short women and tall men for and 1.2° between short women and average men for . A significant effect of the pedal position was also found on . The average maximum difference between the six pedal positions was also small, about 2°.

Intermediate levels of pedal force exertion and force perception (ExpPcp)

The pedal force perception was only performed on the pedal position P2F, which was at the end travel position from an average seat height. The subjects were asked to apply a pedal according to five force level terms from the Borg's CR10 scale: very low, low, medium, high and maximum. In the following section, the pedal force resultants and directions were investigated. The values of the pedal force components (tangential, normal and transversal)

according to the force level were presented in Appendix. Moreover, the postural adjustment of the subjects according to force level was also investigated.

3.2.i Pedal force resultant

Table 36 shows the means and standard deviations of the force resultant according to the force level for each group of subjects. As expected, the force resultant increased significantly with the force level. Besides, it can be noticed that, as in ExpMax, the short women had lower pedal force values for each force level compared to the men. On average, the pedal forces at each force level for the male subjects were two times as high as the short females.

3.2.ii Pedal force direction

As for the analysis of the maximum pedal force exertion, , the pedal force direction in the XZ plane was investigated as well as the deviation angles and in the plane XY. The means and standard deviations of , and are presented in Table 38.

Table 38: Means and standard deviations of the Force/hip ankle axis angle for maximum pedal force exertion.

(deg)

Very low -15.1 ± 8 3.3 ± 3 -1.1 ± 1 Low -12.2 ± 6.4 4 ± 2.7 -1.5 ± 0.9 Medium -9.5 ± 6.9 4.4 ± 2.7 -1.3 ± 1.1 High -6.9 ± 5.5 5 ± 1.9 -1.7 ± 0.9 Maximum -6.7 ± 5.7 5.6 ± 3 -1.6 ± 1.1 Short women -7.1 ± 6.6 6 ± 2.3 -2.5 ± 0.9 Average men -8.5 ± 7.1 4.7 ± 2.1 -1.5 ± 0.9 Tall men -12.1 ± 6. force level. This means that the pedal force direction got closer to the Hip-P App axis when increasing force level. On average, short women had also pedal force direction closer to the Hip-P App axis than the men with an average angle of -7° versus -8.5° and -12.1° for respectively average and tall men.

About the transversal deviation of the pedal force, the subjects did not exert force in the same direction as the Hip-P App axis in the XY plane. On average and were 4.7°

(leftward direction in driver's point of view) and -1.8° (rightward direction in driver's point of view) for respectively. This observation is the same as in the analysis of maximum pedal force exertion. Moreover increased with the force level but the maximum difference was only 2.3°. For , the effect of force level was also significant but the maximum difference between the mean values was only 0.6°.

3.2.iii Postural adjustment

The aim of this part of the analysis of pedal force perception was to look at how posture changed according to force level. As in DHErgo experiment analysis, whole body motions were reconstructed by an inverse kinematic procedure using the kinematic model of RAMSIS™ manikin. Postures of a digital human model (DHM) are fully described by a set of joint angles. Thanks to visual inspection, it was observed that the subjects tended to rise up from the seat when increasing force level. Consequently, the postural adjustment was estimated first by considering the displacement of the pelvis joint GHZ of RAMSIS™ manikin according to the force level, and then by considering the lower joint angles.

The displacement of the pelvis on x-, y-and z-axis of the mock-up coordinate system was calculated for each subject and each force level with respect to a rest posture recorded prior to the session of the force perception trials.

The means and standard deviations of the pelvis displacement on x-, y-and z-axis for each group of subjects according to the force level are summarized in Appendix. Both force level and group of subjects had significant effects on the postural adjustment. Table 39 shows the pelvis displacements for all subjects for each force level. The means and standard deviations of these four joint angles for each group of subjects according to the force level are summarized in Appendix (Table 51 to Table 54). The force level affected significantly these four joint angles, especially for the knee flexion/extension angles. The group of subjects had only an effect on hip and knee flexion/extension angles.

Table 40 shows the means and standard deviations of hip abduction/adduction angle, hip, knee and ankle flexion/extension angles for all subjects according to the force level. First, it can be observed that the hip adduction increased significantly only at the maximum force level. For the intermediate force levels, the mean value of the abduction/adduction angle is between 5-6°, whereas it increased at 8.5° when at the maximum force level. Nevertheless the variation of this angle in function of force level is small with a maximum gap about 3.6° on average. The flexion/extension angles of the hip, knee and ankle joints decreased with force level. Their changes were -9.5°, -12.2° and -3° respectively for hip, knee and ankle from the rest to maximum force postures, showing that the subjects extended their left leg with the increase of force level. Among the three joints of the lower limb, high angle variations were observed for hip and knee. The ankle flexion/extension angle did not vary much with the increase of force level. Concerning the effects of subject group, it can also be noticed that the hip and knee flexion/extension angles of short women were significantly smaller than the men (Figure 49). It means that short women had their left leg significantly more extended than the men. The analysis of the maximum pedal force exertion showed that globally the force exerted on the mid-travel pedal positions were higher than the force exerted on the end-travel position.

This may be explained by the muscle force depends on the muscle length and therefore on the angle of the joint it acts on [START_REF] Kroemer | Assessment of human muscle strength for engineering purposes: a review of the basics[END_REF]. Active muscle force or muscle tension, i.e. muscle force due to muscle contraction, is maximal when the muscle is at its resting length or optimal length (Figure 50). The active muscle force decreases when the muscle is shorten or elongated. Then the muscles cannot provide maximal force when a joint get in a position close to the upper or lower limit of its ROM. Thus, the joint strength decreases. As a consequence, in case of pedal force exertion, the subjects had globally a more flexed leg for the mid-travel position and therefore had higher force capacity (see in Appendix for hip, knee and ankle flexion/extension angles). This may also explain the significant difference of pedal force direction between mid-travel and end-travel positions. Indeed, Figure 51 shows an average pedal force direction and left lower limb posture in the XZ plane for a pedal positioned at mid-travel (plain) and end-travel (transparent). The postural adjustment could be explained by the need of reducing the joint load when increasing force exertion level, because the moment arms of the pedal force at the knee and hip were reduced by extending the leg.

Finally, for all force levels, pedal force exertion was directed leftward whereas the Hip-P app axis was oriented rightward. For the Hip-P app axis, it can be noticed that the mean angle between -1° and -2°, which means that this axis was on average almost parallel to the x-axis of the mock-up reference frame. The orientation of the pedal force on the left was in agreement with the orientation observed in the DHErgo experiment. Moreover the leftward deviation of the pedal force increased with the force level. Results from both FAC and DHErgo experiments suggested that there may be a preferred pedal force direction directed leftward. This could be resulted from the small displacement of the pelvis on the right when increasing the force level.

In summary, the hypothesis of minimizing joint loads seems to explain not only the pedal force exertion control as already suggested by [START_REF] Wang | Experimental investigation and biomechanical analysis of lower limb movements for clutch pedal operation[END_REF], but also the postural adjustment.

Pedal force control simulation

In what follows, we'll verify if this hypothesis is valid for different anthropometric dimensions, pedal positions and force levels. Moreover, the biomechanical model proposed by [START_REF] Wang | Experimental investigation and biomechanical analysis of lower limb movements for clutch pedal operation[END_REF] analyzed the pedal direction in 2D whereas a 3D approach should be used in the present study as the transversal forces being hardly negligible. It should also be interesting to see whether or not the lateral deviation of the pedal force could also be explained by minimizing joint torques. As the postural adjustment and the force direction control seem to be strongly connected, two simulation problems were defined:

Simulation of the force direction with a given posture for a given pedal (imposed

tangential force) [Sim1]
Simulation of the posture with an imposed pedal force (both force resultant and

direction are imposed) [Sim2]
For both simulation problems, the minimization of joint torques as objective function can be formulated as: As a result, the objective function G can be expressed as (Eq. 5): … … (Eq.5)

Simulation of the pedal force direction [Sim1]

The goal in Sim1 is to find the force direction that minimizes the joint torques caused by the pedal force. In this problem, observed postures for a pedal position as well as the pedal force in the rest posture were used as inputs for simulation. Therefore, and in Eq. 4

were constants. The unknown in Sim1 is the pedal force and is constrained as:

The problem in Sim1 can be summarized as:

Find and so as to minimize G (Eq.5)

The simulation was performed using the Matlab © function fmincon. Simulated and experimental force directions were compared in Figure 52 and Figure 53 for both ExpMax and ExpPcp. The values of simulated pedal force directions, normal and transversal forces are presented in Appendix (Table 58 andTable 59).

Simulation of the postural adjustment [Sim2]

The goal of the algorithm Sim2 is to find the postural adjustment that minimizes the joint torques generated by an imposed pedal force. Compared to the problem of Sim1, the pedal force, the pedal position and the pedal force in the rest posture were constants in Sim2 and the unknown variable was the posture. From experimental data, it was found that the subjects adjusted their postures according to the force level by moving the pelvis backward and upward. The main simulation issue is to manage the contacts between the seat and the subject, o Hyp 3 -Displacement of the hip joint: in order to limit the number of DoFs in the simulation, it was considered that the pelvis was translating without any rotation and so that the displacement of the left hip joint in the sagittal plane was close to the pelvis's one.

o Hyp. 4 -Minimization of the hip displacement: by definition, the minimization of the joint torques aims at adjusting the posture in order to get a Hip-P App axis as close as possible to the pedal force direction regardless to the pedal force magnitude. Consequently, a term regarding the minimization of the hip displacement was added to the objective function in order to consider the need of reducing the pressure between body and seat when moving the pelvis.

The problem in Sim2 can be summarized as Eq. 6:

Find such that

JT and Dis are the weight coefficients attributed respectively to the minimization of the joint torques and to the minimization of the hip displacement in the objective function H. In this study, it was considered that the minimization of the joint torques had priority on the minimization of the displacement and the following values for the weights were chosen: JT = 1 and Dis = ½. Moreover it was decided to limit the displacement of the hip joint as: mm. Actually, the maximum displacement observed experimentally was around 85 mm. The limit value in the simulation was fixed at 100mm in order not to constrain the results.

As for Sim1, Matlab © function fmincon was used. But contrary to Sim1, Sim2 required another optimization to solve the minimization problem. Indeed, the displacement of the left hip joint induces necessarily a change in the hip, knee and ankle joint angles in order to keep the contact foot/pedal. It also causes a change in the position of the hip, knee and ankle joint centers, which are necessary to compute the joint torques in Eq. 6. This is a typical inverse kinematics problem. 7 DoFs were considered in the kinematic model as in Sim1 and the position of the target was considered in 3D. The inverse kinematics problem was therefore under-constrained (more variables than unknowns) and was solved using a classic iterative resolution based on the Jacobian pseudo-inverse. At each iteration, the joint limits were verified. If a joint angle was at its limit, the corresponding DoF was blocked, i.e. the value of the joint angle was fixed at its limit values. Then the column of the Jacobian matrix corresponding to the blocked DoF is nullified and the Jacobian pseudo-inverse is recomputed.

The joint angles are then computed and the distance between the target and the end-effector is updated. The joint limits values from RAMSIS™ were considered, which were based according to [START_REF] Kapandji | Physiologie articulaire[END_REF] (see in Appendix). 42 shows the simulated and the experimental flexion/extension joint angles according to the force level. The simulation gives quite good prediction of the three main joint angles. Other joint angles (hip abduction/adduction and axial rotation, knee axial rotation and ankle abduction/adduction) were almost kept unchanged as expected (Table 43), because the displacement of the hip joint was constrained on line in the sagittal plane. Very low -13,2 ± 8,5 5,1 ± 5,7 0 ± 6,7 -1,6 ± 4,1 Low -13,2 ± 8,5 5,1 ± 5,7 0 ± 6,7 -1,6 ± 4,1 Medium -13,2 ± 8,5 5 ± 5,8 -0.1 ± 6,7 -1,5 ± 4,1 High -13,5 ± 8,4 4,6 ± 5,8 0,2 ± 6,5 -1,3 ± 4 Maximum -13,2 ± 8,5 5 ± 5,8 -0.1 ± 6,7 -1,5 ± 4,1

5.3

Discussions of the two simulation methods

In summary, the results of Sim1 and Sim2 confirm the hypothesis of minimization of joint torque for pedal force direction control and postural adjustment when changing force level.

However, Sim1 predicted a rightward (in the driver's point of view) lateral deviation whereas the experimental data showed a leftward lateral deviation.

The analysis of the results of both simulation problems (pedal force direction control and postural change with force level) showed some limitations.

First the simulation of the pedal force direction in the XZ plane showed a mean difference of about 3° between the simulated and the experimental directions. This difference resulted from the underestimation of the normal component of the pedal force (see in Appendix). Although For hip, knee and ankle joints, the joint moments and respectively at rest position and when pressing the pedal can be formalized as:

For hip, For knee, For ankle, Then, the joint torques computed , and in the simulation can be expressed as:

As a consequence, by removing F Rest , the effect of the contact between the seat and the thigh was partly removed. However, the computation of hip joint torque in the simulation did not take into account the possible increase of the contact forces with the force level. Therefore this increase would have an effect on the hip moment calculation.

Secondly, then the simulation of pedal direction for average men showed better results than the other groups. This could be due to the fact that the maximum isometric joint torque values used in the algorithm were average values based on [START_REF] Chaffin | Occupational Biomechanics, 3 rd Edition[END_REF] for the flexion/extension torques, on [START_REF] Delp | Surgery simulation: a computer graphics system to analyze and design musculoskeletal reconstructions of the lower limb[END_REF] for the other degrees of freedom. The data from [START_REF] Chaffin | Occupational Biomechanics, 3 rd Edition[END_REF] were average values from experimental data collection whereas the data from [START_REF] Delp | Surgery simulation: a computer graphics system to analyze and design musculoskeletal reconstructions of the lower limb[END_REF] were based the performance of his musculoskeletal model of an average man. Globally, the data used did not take into account the gender, the age or the anthropometry of the subjects. It could explain why the results are better for the average men.

Thirdly, the differences in the lateral deviation in the XY were found between the simulation and the experiment. Indeed, the simulation predicted a rightward (in the driver's point of view) lateral deviation whereas the experimental data showed a leftward lateral deviation. To evaluate the behavior of the simulation, the distance of the left knee to the vertical plane containing the Hip-P App axis was computed (Table 44). For both maximum pedal force exertion and pedal force perception trials, the average knee distance was negative, meaning that the knee was positioned leftward compared to the HipP App axis. In order to minimize the hip abduction/adduction and axial rotation torques, the pedal reaction force had also to be directed leftward compared to the HipP App axis. As a consequence, the force applied on the pedal was directed rightward in the simulation (Figure 56). The results of the simulation were therefore consistent with the posture and the criterion of optimization, i.e. minimization of the joint torques.

6C o n c l u s i o n

The analysis of maximum pedal force exertions showed that higher forces were exerted for mid-travel pedal positions than for en-travel pedal positions. The more extended the leg is, the closer to the Hip-P App axis the pedal force direction. Pedal force capacities were also found dependent on the gender. On the opposite, pedal force perception law did not depend on the group of subjects but only on normalized force level. With the increase of the force level, all subjects adjusted their posture by moving the pelvis backward, upward and slightly rightward and the pedal force direction tended to get closer to the HipP app axis. Maximum and intermediate force levels pedal force exertion were directed leftward in the driver's point of view whereas the HipP app axis were directed little rightward.

The present work illustrates that the hypothesis of minimization of the joint torques suggested by [START_REF] Wang | Experimental investigation and biomechanical analysis of lower limb movements for clutch pedal operation[END_REF] was able to explain the contribution of the normal force component to control the pedal force direction. The simulation of the pedal force direction also showed that the lateral deviation of the pedal force was not explained by this criterion of minimization.

This preferred pedal force direction in the XY plane has to be controlled by other mechanisms and needs further investigation. Then the simulation of the postural adjustment showed that the minimization of the joint torques explained the displacements observed experimentally.

This simulation also showed that the postural adjustment was the result of a compromise between reducing the joint load and displacing the pelvis, especially for the intermediate force levels. The multi-objective function used in the simulation suggested that the need of reducing the joint load may increase with the force exertion level.

Finally, this study demonstrates the strong relationship between, posture, force capacity and force direction. This study also shows the need of a more realistic modeling the contacts with seat (seat-thigh contact and back-backrest contact).

Introduction

Most of the existing DHMs, especially the ones used in automotive industry, only allow a kinematical approach of the movement. A task-related motion simulation is therefore based mostly on controlling kinematical parameters such as joint angles. However, it became obvious that the dynamic parameters such as joint torques are key parameters to be considered for understanding the perceived discomfort especially for forceful tasks, such as clutching pedal operation investigated in this PhD thesis. But the analysis of the dynamics of a motion is not limited to the joint forces and torques. Many existing studies focused on the musculoskeletal modeling mainly in the clinical applications field [START_REF] Erdemir | Model-based estimation of muscle forces exerted during movements[END_REF]. But with the recent development of DHM packages including musculoskeletal modeling (Anybody Modeling System™, SIMM™/OpenSim™, LifeMOD™ for example), the application field of these models extended to automotive industry [START_REF] Fraysse | Estimation of the Muscle Efforts of the Lower Limb During a Clutch Pedal Operation[END_REF], estimated muscle forces of the lower limb during a clutch pedal operation).

The analysis of the pedal force control showed that the minimization of joint torque could globally explain the pedal force direction in the XZ plane but not the lateral deviation of the pedal force. In chapter 3, it was showed that the criteria of minimization of joint torques applied better for the "high" and "maximum" force levels than for intermediate levels. [START_REF] Schmidt | Foot force direction in an isometric pushing task: prediction by kinematic and musculoskeletal models[END_REF] showed in case of a fixed bicycle pedal that the use of a musculoskeletal model and the maximization of pedal force could be more effective to predict pedal force direction. The direction of the maximum feasible foot force was determined from a force feasible space (FFS) that represented the range of possible forces the lower limb could theoretically produce. In this study, we are going to explore a musculoskeletal model and to evaluate the minimization of the muscular activity as a potential pedal force control law. This criteria was preferred because of the interpretation regarding the minimization of joint torques may be easier.

The aim of this section was to perform an exploratory investigation of musculoskeletal models in comparison with multi-body model without muscles for a better understanding of the mechanism of force exertion control. This chapter is divided in two parts. First, an introduction to musculoskeletal modeling and muscle force computation was briefly presented. Then the criterion of minimization of the muscular activity was examined for predicting pedal force direction. The results from this criterion were compared with those by minimizing joint torques as well as with experimental observations. Besides, muscular activation patterns were analyzed in order to understand the effect of the proposed muscular optimization criteria.

Muscle force computation

Modeling of the musculoskeletal system of the human body

For most of existing digital human models used for ergonomic applications, such as AnyBody™, SIMM™ or LifeMod™, the human body is usually modeled as a set of rigid bodies, i.e. the skeleton, connected by the joints. Body movement is powered by "motors", i.e.

the muscles. Of all the muscles of the human body, the skeletal muscles are of the most interest as they act to move the segments of the human body by contracting and thereby generate forces on the external environment. Muscles are usually modeled by their line(s) of action. It consists in defining a muscle as a straight line from its point of origin to its point of insertion. Some via-points can be introduced to take into account the deviation of the muscular lines of action by the bony structures. Similarly, the large muscles (the gluteus muscles for example) are modeled by multiple lines of action. The enveloping of deflection surfaces also called wrapping is increasingly used recently. Instead of via-points, the muscles are deflected by virtual surfaces of simple geometric shapes, including cylinders and spheres [START_REF] Dickerson | The relationship between shoulder torques and the perception of muscular effort in loaded reaches[END_REF]Desailly, 2008). This allows a better approximation of the deviations of muscle lines of action.

Modeling of the physiological behavior of the muscle

In engineering terms, the muscle has a behavior that could be described as viscoelastic: visco because its behavior depends on the strain rate and elastic as it recovers its original shape and length at the end of a load [START_REF] Kroemer | Assessment of human muscle strength for engineering purposes: a review of the basics[END_REF].

The muscle strength, also referred as muscle tension, represents the maximal force that a muscle can produce and depends on muscle length (Figure 61). The muscle strength at a given muscle length is composed of an active component due to the muscular contraction and a passive component due to the mechanical characteristics of the muscle-tendon unit structure:

For a muscle i,

The active component of the muscle strength is maximal when the muscle is at its resting length, also called optimal length in the literature. When the muscle shortens, the muscle strength decreases with the muscular contraction as unique contributor. When the muscle is elongated, the active component of the muscle strength decreases while the passive component increases to the structural limits of the muscle-tendon unit. The modeling of the physiological behavior of muscles requires the theory of muscle dynamics. Usually the muscle dynamics includes two parts, activation and contraction dynamics. First, the muscle receives a nervous signal also called excitation. From this excitation there is a time delay before the muscle is in its active state and contraction is enabled. Muscle relaxation is also subjected to a time dely. This process is called activation dynamics and can be modeled as a first-order linear differential equation [START_REF] Manal | A one-parameter neural activation to muscle activation model: estimating isometric joint moments from electromyograms[END_REF][START_REF] Lloyd | An EMG-driven musculoskeletal model to estimate muscle forces and knee joint moments in vivo[END_REF]. Then the contraction dynamics is the dynamical process from the active state of the muscle to force generation to the segments. The most prevalent muscle model used in the literature is the model of Hill [START_REF] Zajac | Muscle and tendon: properties, models, scaling, and application to biomechanics and motor control[END_REF]. In this phenomenological model, the muscle is considered as a contractile element combined with one or more elastic elements in parallel and/or in series. There are three levels of model definition:

Model with one contractile element (Figure 62a) in which only the isometric maximum capacity is defined, i. Several sub problems can be defined from Eq. 7, depending on the unknowns in the problem as well as the data available (see [START_REF] Erdemir | Model-based estimation of muscle forces exerted during movements[END_REF] for a review):

Inverse dynamics and static optimization: the muscle forces are computed from a measured motion (q and E are known, f MT are unknown). In this approach, the muscle is considered as a simple force actuator and the optimization is linear.

Forward dynamics: a motion is computed from known muscle forces. This approach uses EMG (ElectroMyoGram) data recorded during motion as an input to simulate a motion. Forward dynamics requires therefore a model of activation contraction dynamics to estimate the muscle forces from EMG data (q unknown, E and f MT known). The optimization problem becomes large and non-linear, because it has the segment positions q and its derivatives as unknowns.

Forward dynamics assisted data tracking: in this approach, q, E and f MT are known.

Rather than solving the equation Eq. 7 using the inverse dynamics approach, an initial set of muscle activations are fed into a forward dynamics model of the musculoskeletal system. The solution is optimized using experimental data to find the muscle activations that best reproduce the experimental kinematics.

Optimal control strategies: this approach is close to forward dynamics assisted data tracking as it is also based on a forward dynamics model. However, in this approach, the optimisation criterion is not limited to fit the experimental kinematics. Other criterion such as the minimization of the energetic cost of a motion [START_REF] Anderson | Dynamic optimization of human walking[END_REF]) can be used.

Another method was also proposed recently DeGroote et al ( 2009):

Physiological inverse dynamics: as the inverse dynamics and static optimization approach, the muscle forces are computed from a measured motion (q and E are known, f MT are unknown) but muscle dynamics with both activation and contraction dynamics is considered. This optimization problem is therefore non-linear, muscle dynamics introducing differential equations.

The inverse dynamics approach with static optimization is widely used, as the movement parameters q and external forces E can be easily measured experimentally using a motion capture system and force sensors. In this study, motion and external contact force data are available for the muscle force computation using an inverse dynamics approach. Therefore, only this approach was developed.

In the inverse dynamics approach with static optimization, joint torques are first calculated using Eq. 8:

(Eq. 8)

Joint torques T MT are directly computed recursively at each joint by isolating each body segment one by one, starting from the most distal (on which the external forces are measured)

to the most proximal. The muscle forces are calculated by solving Eq. 9:

(Eq. 9)

However, the calculation of the muscle forces is not unique. Indeed, the problem is underdetermined, which means that there are more unknowns (i.e. the muscles) than number of equations (i.e. degrees of freedom of the system). As a consequence, the matrix R is not invertible and the problem requires using optimization. The optimization problem can be formalized as:

With f Max the maximum muscle force capacities of the considered muscles (g,h) additional constraints depending on the specific requirements of the joint under investigation.

There are several minimization criteria J in the literature such as [START_REF] Erdemir | Model-based estimation of muscle forces exerted during movements[END_REF]:

Minimization of the sum of the muscle forces or squared muscle forces Minimization of the sum of n th power of muscle stresses Minimization of the sum of instantaneous muscle power Minimization of the maximal relative muscle load … One of the most frequently used criteria is the minimization of the sum of squared muscle stresses, i.e. muscle force divided by the physiological cross section area (PCSA) of the muscle [START_REF] Crowninshield | A physiologically based criterion of muscle force prediction in locomotion[END_REF][START_REF] Kaufman | Physiological prediction of muscle forces-II. Application to isokinetic exercise[END_REF]:

One of the advantages of this criterion is that the optimization problem becomes convex.

Indeed, J is quadratic regarding f MT and the constraint is linear. Therefore, the problem always has a unique global solution.

3 Pedal force direction control by minimizing muscular activities

Description of the musculoskeletal model used

A musculoskeletal model of the lower limb, developed at LBMC by Fraysse (2009) (Figure 64), was used in this work. Bone geometry was based on the data collected within the European project HUMOS 2 [START_REF] Vezin | Development of a set of numerical human models for safety[END_REF] and is composed of four rigid segments: pelvis, femur + patella, tibia + fibula and foot. as their re

The mo actions

The underlined the figure.

).

espective d numbers

Muscles were modeled by their line(s) of action, i.e. 1D element. Muscles' origin, insertion and via-points were based on the lower limb model developed by [START_REF] Delp | Surgery simulation: a computer graphics system to analyze and design musculoskeletal reconstructions of the lower limb[END_REF] and, if necessary, adjusted to the bone geometry from HUMOS 2 on a case by case basis. Indeed, these adjustments were important to avoid incoherencies compared to the anatomic description of the muscles. The coordinates of each muscle's origin, insertion and via-points were expressed in the local coordinate system of the reference bone segment. For example, gluteus maximus's origin was expressed in the pelvis local coordinate system and its insertion in the femur local coordinate system.

For each muscle, a model with one contractile element was chosen. Force-length and forcevelocity relationships, tendons, as well as excitation-contraction dynamics were not considered. Only the maximum isometric muscle force was considered. Muscles PCSAs (physiological cross section areas, i.e. area of the cross-sections perpendicular to the muscle fibers) were taken from data reported by [START_REF] Thorpe | Stresses in human leg muscles in running and jumping determined by force plate analysis and from published magnetic resonance images[END_REF]. An inverse dynamics with static optimization approach was considered to compute the muscle forces. The computation of the muscle forces in this approach is an underdetermined problem (more unknowns than equations) requiring a resolution by optimization. The objective function used to compute the muscle forces was the minimization of squared muscle stresses, [START_REF] Crowninshield | A physiologically based criterion of muscle force prediction in locomotion[END_REF][START_REF] Kaufman | Physiological prediction of muscle forces-II. Application to isokinetic exercise[END_REF].

The musculoskeletal model was evaluated by [START_REF] Fraysse | Estimation des activités musculaires au cours du mouvement en vue d'applications ergonomiques[END_REF] regarding the definition of the muscles by the analysis of the muscular lever arms and regarding the performance to reproduce joint maximal voluntary force by comparison to literature data [START_REF] Chaffin | Occupational Biomechanics, 3 rd Edition[END_REF]. The muscular lever arms were showed in agreement with the physiological functions of each muscle. The prediction of joint maximal voluntary force was in agreement with the literature for average joint positions, but it was less accurate for joint positions close to the joint limits, especially for ankle extension. In the case of the clutch pedal operation, extreme joint positions could happen at the end of the pedal depression. However, the behavior of the model in estimating muscle forces during clutch pedal operation was also evaluated by [START_REF] Fraysse | Estimation of the Muscle Efforts of the Lower Limb During a Clutch Pedal Operation[END_REF] using the data from the experiment of [START_REF] Wang | Experimental investigation and biomechanical analysis of lower limb movements for clutch pedal operation[END_REF]. The activation patterns of the iliopsoas, the rectus femoris and the soleus were in agreement regarding their respective physiological function and the task. The patterns were not compared to EMG data because this type of data was not available..

Optimization problem

The aim was to explain the pedal force control by the minimization of the muscular activity as a potential pedal force control law. The optimization problem is then to find the pedal force direction that minimizes the muscular activities and it can be formalized as in Eq. 10:

With

, the muscular activation level of the muscle i , the force from the muscle i, , the maximum force capacity of the muscle i.

The muscle forces f i were computed using the inverse dynamics with static optimization approach implemented in the musculoskeletal model.

In the present problem, both the data from ExpMax and ExpPcp of the FAC experiment were used. The joint angles, the pedal position and the pedal force in the rest posture were known.

The design variable , i.e. the pedal force simulated by minimizing the muscular activity, was constrained in the same way as in the pedal force direction simulation using the minimization of the joint torques, i.e. conservation of the tangential component, and the joint torques necessary to the muscle forces estimation were computed as in the previous simulation.

Here, only the joint torques generated by the muscles were considered in the muscle force computation. As the hip is modeled as a ball joint, it is considered that all the three hip joint torques are generated by the muscles. At the knee, [START_REF] Lloyd | Strategies of muscular support of varus and valgus isometric loads at the human knee[END_REF] showed that knee muscles contraction provided only 11-14% of the abduction/adduction moment. As a consequence, it can be considered that the muscles do not generate the knee joint abduction/adduction moment but only flexion/extension and axial rotation moments. The same consideration can be applied for the ankle axial rotation moment as this moment is prevented by the bony structures of the ankle (shape of tibio-tarsal joint).

Then, a common value of the proportional factor K PCSA was chosen to estimate the maximum force capacity of each muscle that would constrain the muscle forces f i when computed by the static optimization. The value of K PCSA was chosen such that the constraints on the muscle forces f i allow the static optimization to find a solution. The simulation was therefore tested using the data of the maximum pedal force exertion from the subject (subject 6) who had exerted the highest pedal force in ExpMax. By trial and test, it was found that K PCSA = 50 N.cm -2 allow the muscle force computation for this subject. As a consequence, this value of K PCSA was applied to all subjects.

Finally, the geometry of the musculoskeletal model (e.g. the lever arm) was scaled to each subject of the Ifsttar/Renault experiment. The segments of the bone geometry were scaled according the dimensions of the corresponding body segment in the individualized RAMSIS manikin. As the muscles' origin, insertion and via-points were expressed in their respective reference bone segment local coordinate system, the same geometry-scaling factor was used to calculate the new coordinates of the muscles' characteristic points.

4R e s u l t s 4.1 Force force direction

The pedal force directions simulated by minimizing W (Eq. 10) ( and ). were compared with experimental force directions ( and ). The two sets of simulated data with the inputs from the maximum force exertion (ExpMax) and force perception (ExpPcp) trials, were statistically analyzed separately. , the simulated pedal force direction in XZ plane, and , the lateral deviation in XY plane were estimated using the same definition as in the previous section. The values of simulated normal and transversal force components as well as the experimental and simulated pedal force direction in XZ and XY planes are presented in Appendix. Figure 66 shows the comparison of pedal force direction in XZ plane between simulated and experimental data. For comparison purpose, the pedal force directions simulated by minimizing the joint torques (Sim1 in Chapter 2) ( and ) are also presented. The proposed criterion is consistent with the minimization of the joint torques, at least at maximum force level. Indeed, at maximum force level, it can be considered that all muscles, which had a physiological function in agreement with the task, are activated. Thus minimizing the joint torques can be interpreted as minimizing the muscle forces at the joint However, for intermediate force level, all muscles with a physiological function in agreement with the task may not be activated and there may be muscular recruitment that produces the necessary force at the joint minimizing the muscular activation. The results on the activation patterns showed that the minimization of the joint torques activated more muscles and in higher levels than the muscular minimization criterion. It may suggest that low intermediate force levels, reducing the joint load may be a motor control strategy more costly than minimizing the muscular activity. This would be consistent with the principle of minimum work, observed in biomechanical analysis of leg movements during clutch pedal operation [START_REF] Wang | Experimental investigation and biomechanical analysis of lower limb movements for clutch pedal operation[END_REF] and can explain why the minimization of muscular activity had a pedal force direction close to experimental one in XZ plane.

For the lateral deviation, the comparison of the muscle activation patterns for maximum pedal force exertion showed that the experimental force caused on average higher activation levels than the simulated pedal force, especially in the hip and ankle abductor muscles. An explanation can found in the analysis of the muscular lever-arms. Indeed, the muscle force computation depends highly on the lever-arms of the muscles (cf. Eq. 9 in this chapter).

Considering that the lateral deviation is mainly due to hip abduction or adduction, Table 60 shows the lever-arm values for the main hip adductors and abductors of the selected subject for mid-travel and end-travel pedal position. It can be noticed that the lever-arms for adduction are globally larger than the one for abduction. As a consequence, lower activation levels would be required from adductors than from the abductors to generate respectively adduction and abduction torques of the same magnitude. Minimizing the muscular activity would therefore rather support the adduction regarding the abduction if not constrained and thus predict a rightward pedal force direction than a leftward one.

Then, the behavior of the model was evaluated by [START_REF] Fraysse | Estimation des activités musculaires au cours du mouvement en vue d'applications ergonomiques[END_REF] and showed that physiological functions of the muscles were respected. As a matter of fact, the analysis of the muscular patterns was consistent with the analysis of the pedal force direction. However, the muscular activation patterns were not compared to experimental EMG patterns in order to validate the output of the muscle force computation. In the Ifsttar/Renault experiment, no EMG were recorded but EMG were placed on biceps femoris (hip extensor and knee flexor), rectus femoris (hip flexor and knee extensor), tibialis anterior (ankle flexor) and lateral and medial gastrocnemius (knee flexor and ankle extensor) in the DHErgo experiment (Figure 70). However, these EMG data were not comparable to the output of the muscle force computation. Indeed, the muscle force computation depends on the joint torques computation by inverse dynamics and as the seat/thigh contact force was not estimated during the clutch pedal operation in DHErgo experiment, the hip joint torques used in the muscle force computation were not reliable. Besides the most important muscles of the lower limb such as rectus femoris, biceps femoris and the lateral and medial gastrocnemius are bi-articular muscles, i.e. crossing two joints. As a result, an unreliable estimation of the hip joint torque would also affect the muscle recruitment at the knee and the ankle. Therefore the available EMG data were not suitable to validate the he output of the muscle force computation.

Finally, the present work aimed at investigating the potential contributions of the musculoskeletal modeling for the understanding of the pedal force control. The proposed muscular optimization criterion was consistent with the need of reducing the joint load when increasing force exertion level and improved the results of the simulation of pedal force direction for intermediate force levels. However, the preferred pedal force direction in the XY plane was not explained by any of the minimization criteria. The study also showed that the validation of estimated muscle forces is, in particular a major issue, for the musculoskeletal model. Even if all external forces (especially contact forces) were known, a quantitative validation of the muscle force computed may be hard to achieve because the computation depends on the muscle lever-arms and on the maximum muscle force capacity [START_REF] Scovil | Sensitivity of a Hill-based muscle model to perturbations in model parameters[END_REF][START_REF] Redl | Sensitivity of muscle force estimates to variations in muscle-tendon properties[END_REF], which also means that the computation of muscle forces highly depends on the anthropometry and the strength capacity of the subject.

PERSPECTIVES FOR INTEGRATION IN AUTOMOTIVE DESIGN PROCESS 1 Introduction

In this PhD thesis, two research questions for digital human modeling were investigated:

How to propose objective discomfort indicators to help the design engineers in their design selection process How to predict posture taking into account force exertion.

In this section, the main results are summarized. Then, the limitations of the study are discussed and then follow some perspectives for future work. Finally, short term perspective for industrial integration of the present work is proposed.

Biomechanical approach for evaluating motion related discomfort

In this study, the assumption was made that a better comfort may be obtained when people can make their own appropriate adjustments. A motion related discomfort modeling approach based on the concept of less-constrained movement has been proposed and illustrated by a case study of clutch pedal operation. The proposed approach is divided in two steps. First the identification of the relevant biomechanical parameters is performed by comparing imposed and less-constrained movements. The efficiency of this step mainly lies on an experimental design with the appropriate apparatus to control the critical design parameters of the investigated task. Second, the definition of the discomfort indicators is based on the use of cost functions as in many discomfort studies in the literature. Although the proposed approach is generic, the identified discomfort criteria are task-specific and test condition specific.

Indeed, the proposed discomfort indicators may not be applicable to other situations where other pedal design parameters are adjustable. The method was also applied in the DHErgo project to an upper limb task (handbrake pulling) and a whole body movement (car ingress/egress).

In this work, less-constrained movements were used for identifying relevant biomechanical parameters in order to define discomfort indicators. In case of simulation using a DMH, the proposed indicators can be computed to compare several pedal design without the need of less-constrained movements. But the question how to simulate less-constrained motion remains open. Indeed, end-users could be interested in predicting less-constrained configuration. In case of the clutch pedal, the ranges of pedal adjustments observed experimentally were large and depended not only on pedal configuration but also on subject.

A data-based approach as the one implemented in RPx could be proposed to predict less-constrained motion. The general idea would be to separate imposed and less-constrained motion in the motion database. As the pedal position should remain free, the task descriptors should the other controlled parameters of the task (subject characteristics, seat height, pedal resistance, travel length …). Then, according to a new scenario (i.e. new task descriptors), a referential motion would be selected in the less-constrained motion database. Finally, the lessconstrained movement for the new scenario could be predicted modifying the less-constrained movement of the referential pedal position. However such method would assume that an individual would adjust the pedal in the new scenario as in the referential motion. Actually, the pedal adjustment data from this study agree with this assumption as for example, pedal with lower seat height were lowered on average whereas pedal with higher seat height were moved up. But still, validation would be needed. Moreover, as for all data-based methods, the results would highly depend on the underlying dataset and it would be difficult to extrapolate outside the experimental conditions. dependent on movement, implying that the motion simulation should be experimentally validated when using a DHM. Besides, both kinematic and dynamic parameters should be considered. This is particularly important for the dynamic parameters such as joint torques. In case of automotive control, the necessary force to apply on a control is usually known by the car manufacturer, for example, the pedal resistance along its travel path. But how a person will press the pedal to perform the task is more complex to estimate as the force direction is not strictly imposed by task. Any force that produces a component necessary for overcoming the pedal resistance is theoretically possible. And the computation of the joint torques highly depends on the control of the force direction. This is what connected the two parts of this PhD thesis. Biomechanics based discomfort modeling is one of the advantage of using a DHM, but its use is limited by the performance of DHM to simulate real task-related motions.

Force exertion task control strategy and simulation

As a first step to improve the methods of posture prediction of force exertion-task, the mechanism of the force exertion on automotive control was investigated through the example of the clutch pedal. Using experimental data and biomechanical simulation, two aspects of the force exertion were investigated: the control of the force direction and the control of the posture. Interestingly, it was found that the pedal force direction and the postural adjustment were mainly explained by the need of reducing the joint load when increasing the force level.

Besides, a simulation approach based on a musculoskeletal model and an optimization criterion minimizing the muscular activity improved the prediction of pedal force direction for low and intermediate force levels when compared to the minimization of joint torques with a multi-body model without muscles. However, it was also showed that the lateral deviation of the pedal force was not explained by any criteria of minimization, which suggests that the lateral force deviation may be controlled by other mechanisms.

The following step to this study would be to merge force direction and posture simulation methods into a unified one. As the study tends to suggest that minimization of joint load explain both force direction and posture adjustment, a first method would be to merge the two algorithms used in this work. Considering that the tangential force is imposed regarding experimental data, this new algorithm could be formalized as to predict both force direction and postural adjustment simultaneity:

Or using a musculoskeletal model:

JT , Dis and Mus are the weight coefficients attributed respectively to the minimization of the joint torques, of the hip displacement and of the muscular activity in their respective objective functions. The design variables of both optimizations are the hip displacement, the normal pedal force and the transversal pedal force. However, due to limited time, we have not tested this unified algorithm for prediction both force direction and posture. We strongly suggest a more realistic modeling the contacts with seat (seat-thigh contact and back-backrest contact). In DHErgo project, estimation of the contact forces was done using pressure maps. But many simplifying hypotheses were also done to estimate the impact of the contact on the hip joint. Currently, finite-element models such as Pam-Comfort, aim to predict the contacts with the seat for a seated individual. Based on finite elements modeling, this type of model takes into account the deformation of the flesh and of the seat regarding the mechanical properties of foam constituting the seat. However, the computation is relatively time consuming. Further developments are therefore needed.

The study also illustrates the strong relationship between posture, force capacity and force direction. Posture and force direction are interdependent and highly depend on the perception of the necessary force regarding the force capacity. Knowing the strength of all joints in every posture would allow to predict realistic force direction and posture regarding the force requirements for any task but also maximum force exertion capacity. However, joint strength is currently collected only using a heavy time-consuming experimental protocol. Considering that muscular strength vary greatly depending on a high number of factors such as posture, joint or the limb considered, subject, a large amount of data is required to get a full characterization of an individual. A solution to the characterization of joint strength may come from the use of musculoskeletal model. Indeed, considering that the muscles lever arms acting on a joint depends on posture; a few but well-chosen maximum joint torque data could be enough to scale some of the musculoskeletal model's parameters and therefore, the scaled model should be able to predict maximum joint torque in new postures. An attempt to develop such method was performed during this PhD thesis (Pannetier et al., 2011). The results were encouraging but far from being used in simulation and further investigations are needed.

4

Perspectives for integration in automotive design process

In the current state of knowledge, the simulation of pedal force exertion using optimization criteria may not fulfill the car manufacturers' expectations. But using the data available in the FAC project, a data-based approach could be proposed. First the posture database has to be structured considering the usual motion descriptors as well as the force level perception. Then using a motion modification method as the one implemented in RPx, the posture could be predicted. Second, an interpolation method such as the one proposed by Wang et al. (2010) could be used to estimate the force magnitude and direction within the experimental space.

This method could be implemented for clutch pedal but also for handbrake and gear lever as these two controls were also investigated in the FAC project. In case of the clutch pedal, as the posture and the pedal force are estimated, the discomfort indicators could also be estimated. Using this approach, a realistic pedal force exertion task and its discomfort assessment could be proposed. The main limitation of this method would be the same as all data-based method, which is the high dependency regarding the underlying dataset.

Méthodes et procédures expérimentales

2.2.i Mesures expérimentales

Un conformateur à géométrie variable possédant les éléments importants de l'habitacle

Analyse de la gêne ressentie

Le questionnaire d'inconfort utilisé dans l'expérimentation avait pour but de recueillir la gêne ressentie au cours du mouvement de débrayage à l'aide d'une échelle de notation, mais aussi d'obtenir des indications des effets des paramètres de configuration de la pédale (siège, position de la pédale en début/fin de phase d'appui, longueur/inclinaison de la course, résistance de la pédale) sur l'inconfort à travers des questions à choix multiples. En outre, dans le cadre de l'utilisation du concept de mouvement moins contraint, l'analyse des réponses devrait permettre d'identifier quel(s) réglage(s) ont été fait par les sujets ainsi que d'expliquer l'effet de ces ajustements sur la perception de la tâche. Les réponses au questionnaire ont donc été analysées en fonction de trois variables indépendantes : le groupe de sujets (âgés/jeunes femmes/hommes), la configuration de pédale et le type de configuration (imposée ou moins contrainte).

2.3.i Evaluation des paramètres de configuration

Pour les questions à choix multiples, les effets des trois variables (groupe de sujets, la configuration, le type de configuration) ont été analysés à l'aide des tableaux de fréquence.

2.5.iii Effort sur pédale et inconfort

La configuration a eu des effets importants sur l'effort appliqué sur la pédale (résultante, composantes et direction) et sur les couples articulaires (genou et cheville). L'analyse de l'inconfort a montré que la configuration a également eu des effets importants sur la perception de la résistance de la pédale ainsi que sur les notes d'inconfort. Par conséquent, les corrélations entre les forces tangentielle et normale, la direction de la force, les couples articulaires (genou et cheville) et les notes CP50 ont été étudiés (Table 65). Les variations d'angles entre la posture de repos et celle d'effort maximum étaient de -9.5°, -12.2° et -3° respectivement pour la hanche, le genou et la cheville, montrant ainsi que les sujets ont étendu leur jambe gauche avec l'augmentation du niveau de la force. Parmi les trois articulations du membre inférieur, des variations angulaires élevées ont été observées pour la hanche et du genou à la différence de la cheville. Il peut également être remarqué que les angles de flexion/extension de la hanche et du genou des femmes de petite taille étaient significativement plus faibles que ceux des hommes, ce qui traduit le fait que les femmes ont en moyenne plus tendu leur jambe gauche que les hommes.

3.4

Simulation de la direction d'effort et de l'ajustement postural

3.4.i

Hypothèse de simulation [START_REF] Wang | Experimental investigation and biomechanical analysis of lower limb movements for clutch pedal operation[END_REF] ont suggéré dans leur étude que le contrôle de la direction d'effort sur une pédale reposait sur le principe de minimisation des couples articulaires du membre inférieur.

Néanmoins, l'hypothèse n'a été validée qu'avec un modèle biomécanique 2D alors les résultats expérimentaux montrent que la déviation latérale de la force n'est pas négligeable.

Par ailleurs, les observations expérimentales tendent à montrer que l'ajustement postural pourrait aussi s'expliquer par la nécessité de réduire le chargement articulaire. En effet, lorsque l'on augmente le niveau d'effort, l'extension de la jambe tend à vouloir diminuer les bras de levier de la force au niveau du genou et de la hanche.

3.4.ii Simulation de la direction d'effort

Le but de cette première simulation est de calculer la direction d'effort à posture et force tangentielle imposées qui minimisent les couples articulaires. Le problème a été formalisé de la façon suivante : En ce qui concerne la déviation latérale de l'effort, la simulation a prédit une orientation vers la droite alors qu'expérimentalement, l'effort était orienté vers la gauche. Ce résultat sousentend que la déviation latérale de l'effort n'est pas contrôlée par la minimisation des couples mais potentiellement par un autre mécanisme que les données actuelles ne permettent pas d'identifier.

3.4.iii Simulation de l'ajustement postural

Le but de cette deuxième simulation est de calculer le déplacement de la hanche gauche à

  Figure 1: D from Dass

  better understand the mechanisms of control of movement and discomfort during force exertion-task, IFSTTAR and Renault started a research project focused on force on automotive controls (FAC hereafter) in early 2008 for collecting data on drivers' capabilities and force perception on automotive controls (pedals, gear lever, and handbrake). From September 2008, a three-year European collaborative project called DHErgo (Digital Humans for Ergonomic design of products) coordinated by IFSTTAR was launched. The consortium composed of five academic partners (the Biomechanics and Ergonomics team from LBMC at IFSTTAR, the Institute of Ergonomics at the Technical University of Munich -LFE-TUM, the CEIT and the Université Libre de Bruxelles -ULB) , two software editors (Human Solutions and ESI) and three car manufacturers (BMW Group, PSA Peugeot Citroen and

  Figure 2: Conceptual model of sitting comfort and discomfort by a) Zhang et al. (1996) and b) De Looze (2003).

Figure 3 :

 3 Figure 3: Conceptual "Dose Response" model by Armstrong et al (1993)

Figure 4 :

 4 Figure 4: Concept of neutral movement(Wang, 2008)

Figure

  Figure 5: Facto

  Figure 5 su

Figure 6 :

 6 Figure 6: Main components and overall flow of the model proposed by Hoffman (2008).

  Figure 7: E loading.

  ......................................................................... 8.11 QUESTIONNAIRE FREQUENCY TABLES ...................................................................... 8.12 QUESTIONNAIRE REPRODUCIBILITY ......................................................................... 8.13 IMPOSED VERSUS LESS CONSTRAINED JOINT ANGLES .................................................. 8.14 DISCOMFORT COST FUNCTION FOR KINEMATIC INDICATORS ........................................ 8.15 DISCOMFORT MODELLING APPROACHES USING DISCOMFORT INDICATORS .....................

Figure 8 :

 8 Figure 8: Active range of motion (ROM) measurements

  Figure 9: Ma

  Figure 1

Figure

  Figure 13: M

  Figure 13

Figure

  Figure 14: Mot

Figure 15 :

 15 Figure 15: Manikin superimposition for subject 5 of FAC experiment on RPx

  Figure 17: a high sea pressure m

  Figure 16

  , i.e. the subject gave three different answers to the same question on the three repetitions = 1 Cond2 2 similar answers, i.e. the subject gave two times the same answers to the same question on the three repetitions = 2 Cond3 3 similar answers, i.e. the subject gave the same answers to the same question on the three repetitions = 3

  Figure 18 Q2H/Q3H pedal late

  Figure 19: Distr

Figure

  Figure 20: Distr

Figure 22 :

 22 Figure 22: Discomfort ratings centered to each participant's mean value in terms of type of configuration

Figure 23 :

 23 Figure 23: Discomfort ratings centered to each participant's mean value in terms of configuration

Figure 24 :

 24 Figure 24: Discomfort ratings in terms of perceived resistance levels (CP50 raw score)

  Figure 25 adduction (*p<0.05,

  05, **p<0.01, *** p<0.001. C: configuration, G: group of subjects, T: Type of configuration

  Figure 27: The variab subjects a

  05, **p<0.01, *** p<0.001. C: configuration, G: group of subjects, T: type of configuration

  1 G***, C***, ± 13.1 -11.4 G***, C*** ± 5.7 *p<0.05, **p<0.01, *** p<0.001. C: configuration, G: group of subjects BMW4 had the lowest joint torque average value, i.e. -12 Nm for knee. PCA1 and REN3 had the highest knee joint torque mean values, -25.4 Nm and -27.4 Nm. BMW1, BMW2 and PCA2 knee joint torque average values between -17 Nm and -18 Nm. About the ankle joint torques, it can be noticed that the two configurations with higher knee moments, i.e. PCA1 and REN3, are the ones with lower mean ankle moments, respectively -7.4 Nm and -9 Nm.

  Fig

  joint flexion extension angles at the beginning and end of depression. In order to consider lateral position adjustment, there could be several candidate parameters, such as hip abduction-adduction angle, the travel distance from the foot rest position to that at beginning of depression or the lateral position of the left foot at beginning of depression. Due to a small change of pedal lateral position, the variation of hip abduction/adduction angle between imposed and less-constrained configurations was very small. As the foot position on the foot rest before depression was not strictly controlled, a part of variability could be introduced to any parameters that are dependent on foot rest position. Therefore, the foot lateral position was used here for constructing a discomfort indicator. The following discomfort indicators are therefore proposed from the suggested kinematic and dynamic parameters: Seven kinematic indicators: o Two hip flexion angles (IndHipStart and IndHipEnd) respectively at the start and end depression, o Two knee flexion angles (IndKneeStart and IndKneeEnd) o Two ankle flexion angles (IndAnkleStart and IndAnkleEnd), o The left foot position which is characterized by the ball of foot lateral position (yBoF) at the beginning of the depression normalized by half of the subject's pelvis width (IndLPos). Pelvis width is defined as the distance between the two

Figure

  Figure 31: Di

Figure 32

 32 Figure32shows the mean and standard deviation values of four selected indicators and the normalized discomfort for imposed and less-constrained configurations of BMW4, PCA2 and REN3. It can be noticed that discomfort predicted by IndKneeJT and IndLPos agree quite well with the perceived discomfort when comparing the three configurations BMW4, PCA2 and REN3. IndAnkleStart, IndLPos and IndKneeEnd give a good indication of pedal assessment when comparing imposed and less-constrained configurations. InAnkleStart and

  Secondly, individual discomfort indicators which allowed the assessment of the pedal starting and end positions as well as its resistance as well as two approaches on how to combine these indicators were proposed. About the discomfort model the linear regression based on CP50 scores had poor results. This may be explained by the poor reproducibility of the subject's ratings using the CP50 scale. Besides, each subject may not use the same interval of the CP50 scale to estimate a similar discomfort perception. Actually, how to combine these indicators into one global discomfort indicator requires deep expertise in product design. These indicators could provide objective elements for an expert to form a global assessment of different design solutions. But global ratings and the proposed indicators are not opposed to each other and should be considered as complementary tools for the ergonomic assessment of products.

  , 6 parameters have been considered important to configure a clutch pedal: Seat height or H30 Clutch pedal travel length Clutch pedal travel angle Clutch pedal initial position (x-, y-and z-coordinates)

  Figure 33: componen

  sont les parties du corps où vous ressentez le plus d'inconfort ? Entourez-les sur la figure ci-dessous.

Figure

  Figure 38: D

  .................................................................................................................. 7.8 PELVIS DISPLACEMENT FOR SHORT WOMEN, AVERAGE MEN AND TALL MEN ACCORDING TO THE FORCE LEVEL ............................................................................................................ 7.9 VARIATION OF JOINT ANGLES FOR SHORT WOMEN, AVERAGE MEN AND TALL MEN ACCORDING TO THE FORCE LEVEL ...................................................................................... 7.10 HIP, KNEE AND ANKLE FLEXION/EXTENSION ANGLES ACCORDING TO THE PEDAL POSITION AND THE GROUP OF SUBJECTS ........................................................................................... 7.11 FUNCTIONAL MAXIMUM DATA USED IN THE SIMULATION .......................................... 7.12 COMPARISON OF SIMULATED AND EXPERIMENTAL PEDAL FORCE DIRECTIONS ............... 7.13 COMPARISON OF SIMULATED AND EXPERIMENTAL NORMAL AND TRANSVERSAL FORCES .

  were aged from 20 to 44 and had neither musculoskeletal abnormalities nor any history of trauma. All of them had driving experience of more than one year. They were divided into three groups according to stature and gender of French driver population(IFTH, 2006): 10 short females: <1625 mm (5 th percentile of the French driver population) 10 average height males: 1705 -1810 mm (50 th percentile of the French driver population) 10 tall males: >1810 mm (95 th percentile of the French driver population)

Figure 40 :

 40 Figure 40: Experimental mock up for static pedal force exertion experiment

  Fig were synchro

  Figure 43: presented

Figure 45 :

 45 Figure 45: Resultant force mean values in terms of pedal position (a) and of group of subjects (b). Normal force mean values in terms of pedal position (c).

Figure 47 :

 47 Figure 46

  05, **p<0.01, *** p<0.001. G: group of subjects; F: force level Significant effects of both the force level and the group of subjects were found on , and . It can be observed that absolute value of decreased with the level of force, from -15.1° on average for very low force level to -6.7° on average for maximum

  The variations in joint angles of the left lower limb were then analyzed. As the subjects adjusted their posture by moving the hip backward, upward and rightward, four joint angles were considered: Hip, knee and ankle flexion/extension angles which should explain backward and upward postural adjustment, Hip adduction/abduction angle which should explain rightward adjustment.

  Figure 49 flexion/ex

Figure 50 :

 50 Figure 50: Active, passive and total tension within a muscle at different lengths (Kroemer, 1999).

  Figure 51 (transpare

(F

  /Adduction (AA), Flexion/Extension (FE), Axial rotation (R)}is the moment projected on a joint rotation axis (DOF) for a joint. The three lower limb joints (hip, knee and ankle) were considered in the optimization. The kinematic model of the lower limb has 7 DoFs. However, two joint torques were not considered in the objective function:the knee moment in abduction/adduction and the ankle moment in axial rotation, because they were not supposed to contribute to the motion and the force exertion. The joint torque can be computed as: Rest , pedal force in the rest posture, i.e. the contribution of the weight of the leg on the pedalIn the joint torque calculation (Eq. 4), the pedal force recorded at rest position was considered as the action of the body weight on the pedal. Consequently, its contribution to the joint torque, considered as passive, was removed in order to keep only the active contribution of the force exerted on the pedal. The computed joint torques were normalized in the objective function (Eq. 3) using maximum joint torque values from the literature (see in Appendix).

  Figure 52: and

  Figure 53 (respectiv experimen

  especially the contacts trunk/backrest and thigh/seat. Ideally, a surface-surface contact model should be used. But such data were not available in the FAC project. Some simplifying hypotheses were therefore made: o Hyp. 1 -Displacement in the sagittal plane: Experimentally, the rightward displacement of the pelvis was found weaker than the back-and updisplacements. It was decided to consider only the back-and up displacement in the sagittal plane in the simulation.o Hyp. 2 -Constraints on the displacement: In order to consider the contact between the seat backrest and the subject's trunk, it was hypothesized that the displacement was following a line in the sagittal plane. The ratio in the simulation was estimated using the mean value of the same ratio from experimental data, i.e. . Besides and were constrained as positive in order to avoid displacement into the seat.

  Figure 54: Struc

Table 43 :

 43 Means and standard deviations of hip abduction/adduction and axial rotation, knee axial rotation and ankle abduction/adduction angles for all subjects according to the force level in the simulation of postural adjustment (

  Figure 55: W Thigh rep of mass o pedal forc seat/thigh points

  Figure 56:

  Figur

Figure 61 :

 61 Figure 61: Active, passive and total tension within a muscle at different lengths (Kroemer, 1999).

  Figure 6

  Figure 63: compensa generated

  Figure 65 correspon

  Figure 66 minimizat ) fo

  Figure 67: minimizat ) fo

Figure 70 :

 70 Figure 70: Muscles selected for EMG measurement in DHErgo experiment.

  As far as the proposed discomfort indicators are concerned, there are two main issues remained open. First is how to combine the indicators to get a global evaluation of the proposed design. Actually, a global score is usually preferred by the engineers as it allows to quickly differentiate several design alternatives. Our proposed discomfort indicators may be efficient to compare some design propositions but they may not fulfill the expectations of industrials in terms of absolute ergonomic assessment. In this study, the attempt to propose a global score using the indicators and the discomfort ratings showed bad results. The poor reproducibility of the subject's ratings and the small size of subject sample could explain the poor results of the model. To improve the model, new experimentations would be required with a larger subject sample and also an efficient training of the subjects to use a rating scale to express their discomfort perception.[START_REF] Annett | Subjective rating scales: science or art?[END_REF] argued that comfort/discomfort assessment is as much a science as an art. How to combine these indicators into one global discomfort score certainly requires deep expertise in product design. These indicators could provide objective elements for an expert to form a global assessment of different design solutions. In this point of view, the proposed approach base on the definition of relevant objective individual indicators is not opposed but rather complementary to expertise. Second is the dependency of the discomfort indicators computation with the motion simulation. The proposed discomfort indicators are based on biomechanical parameters

  Figure

  Figure 72: adduction flexion/ex

  indicateurs d'inconfort pour le débrayage A partir des précédents résultats d'analyse, plusieurs indicateurs d'inconfort ont été retenus : 7 indicateurs cinématiques, i.e. les angles de flexion/extension en début et fin de course pour la hanche, le genou et la cheville ainsi que la position latérale du pied 2 indicateurs dynamiques, i.e. les couples articulaires du genou et de la cheville en fin de course Les indicateurs cinématiques ont été définis à l'aide de fonctions de coût en terme d'inconfort et les indicateurs dynamiques ont été définis en normalisant les couples articulaires par les valeurs de couples isométriques maximum collectées pour chaque sujet. Ces indicateurs pourraient en particulier servir à la comparaison de différentes configurations de pédale d'embrayage dans le but de sélectionner la meilleur du point de vue de la gêne ressentie. réaliste la posture adoptée par un individu à l'aide d'un mannequin numérique est un des points critiques inhérents à l'utilisation des mannequins numériques pour l'évaluation ergonomique. Pour les tâches impliquant la production d'un effort telles que la manipulation des commandes automobiles, l'individu adapte non seulement sa posture aux contraintes géométriques, mais aussi au niveau qu'il doit appliquer pour réaliser la tâche. La plupart des mannequins numériques existants utilisés dans l'industrie automobile pour l'évaluation ergonomique d'un produit ou d'un poste de travail ont une approche cinématique du mouvement humain sans tenir compte des efforts enjeu. Dans le contexte de la simulation réaliste de la manipulation de commandes automobiles, les stratégies de contrôle de posture et de l'effort sur la pédale ont été étudiées à partir de données expérimentales de capacité d'effort collectées dans le projet FAC, afin de proposer des améliorations des mannequins numériques pour la simulation de tâches automobiles.

  Un conformateur à géométrie variable a été utilisé pour définir différentes configurations de conduite. L'ensemble des éléments du conformateur (siège, volant, plancher, pédale d'embrayage, levier de vitesse et frein à main) ont été positionné autour d'un point H de référence lié au siège, défini à partir du mannequin de référence SAE J826. Trois configurations de pédale d'embrayage ont été sélectionnées par Renault, représentant les trois gammes de véhicules majoritaires chez le constructeur (compact, berline et monospace). Pour chaque configuration, deux positions-clés ont été retenues pour l'expérimentation (milieu et fin de course), donnant ainsi 6 positions statiques à tester par les sujets (Figure74).Trente sujets volontaires ont pris part à l'expérimentation : 10 femmes de petite taille (5 ème centile de la population française en termes de stature), 10 hommes de moyenne taille (50 ème centile) et 10 hommes de grande taille (95 ème centile). Tous les sujets étaient des conducteurs expérimentés et ne présentaient aucun trouble musculo-squelettique. vite que possible et de le maintenir pendant 5 secondes. La valeur moyenne de chaque essai a été calculée à partir de 1.5s à 4.5s. les efforts collectés au cours de l'expérimentation ont été analysés par analyse de la variance suivant 2 variables indépendantes : le groupe de sujet et la position de la pédale pour les données provenant d'ExpMax et le groupe de sujet et le niveau d'effort pour les données d'ExpPcp. En outre, Les efforts enregistrés sur la pédale au cours des essais ont été analysés suivant la décomposition aussi utilisée dans la précédente étude : force tangentielle, normale et latérale.

  Figure 75: d'effort en

  calculés dans la simulation pour les trois articulations du membre inférieur sur 7 degrés de liberté ou DDL : 3 pour la hanche, 2 pour le genou (pas d'abduction/adduction) et 2 pour la cheville (pas de rotation axiale). Ils ont aussi été normalisés dans la fonction objectif G en utilisant des valeurs maximales de couples de la littérature.Les directions d'effort simulées ont été ensuite comparées à celles expérimentales. En ce qui concerne l'angle par rapport à l'axe HP App , les résultats de la simulation ont montré les mêmes tendances observées expérimentalement, i.e. une direction d'effort plus proche de l'axe HP App pour les pédales en fin de course et une direction d'effort se rapprochant de l'axe HP App avec l'augmentation du niveau d'effort. L'écart entre les résultats de simulation et les données expérimentales est en particulier plus important pour les efforts « très faible » et « faible », environ 5°, que pour les efforts « maximum », environ 3°. Cet écart vient d'une sous-estimation par la simulation de la composante normale de l'effort qui contrôle la direction dans le plan sagittal. Cette sous-estimation pourrait venir du fait que les contacts entre le siège et la cuisse au cours des essais n'étaient pas connus.

  direction d'effort imposée qui minimise à la fois les couples articulaires et le déplacement de la hanche. Du fait qu'expérimentalement les sujets se sont principalement reculés et élevés, le déplacement simulé a été contraint dans le plan sagittal sur une droite définie telle que . Ce second problème a donc été formalisé de la façon suivante : Trouver et qui minimisent Les pondérations de la fonction objectif pour les parties minimisant les couples articulaires et le déplacement de la hanche ont été choisies arbitrairement en partant du fait que la minimisation des couples était prioritaire. La limite de déplacement a été fixée à 100 mm en se basant sur les résultats expérimentaux. Par ailleurs, contrairement à la première simulation, une seconde optimisation a été nécessaire pour pouvoir estimer H. En effet, à chaque itération, il est nécessaire de calculer une nouvelle posture qui prenne en compte le déplacement imposé. Ce problème a été résolu en utilisant une méthode de résolution itérative classique basée sur la pseudo-inverse de la matrice jacobienne. Globalement, les mêmes ordres de grandeur de déplacement de la hanche ont été trouvés par simulation comparés aux données expérimentales. En outre, la simulation a correctement prédit le fait que le déplacement de la hanche augmentait avec le niveau de force. La simulation donne également une assez bonne prédiction des trois principaux angles articulaires en fonction du niveau de force. Les résultats montrent que l'ajustement postural est le fruit d'un compromis entre la réduction du chargement articulaire et le déplacement du bassin, en particulier pour les niveaux de force intermédiaires. La fonction multi-objectif utilisée dans la simulation met aussi en avant le fait que le besoin de réduire le chargement articulaire augmenterait avec le niveau d'effort. du contrôle de la direction d'effort dans la partie précédente a montré que la minimisation des couples articulaires pouvait globalement expliquer l'orientation de la force dans le plan sagittal mais pas la déviation latérale. Par ailleurs, il a été montré que le critère de minimisation des couples articulaires donnaient de meilleurs résultats pour les efforts maximum que pour les niveaux d'effort intermédiaires. Dans cette partie, nous allons utiliser un modèle musculo-squelettique et considérer la minimisation de l'activité musculaire comme loi de contrôle moteur de la direction d'effort. Ce critère a été choisi car son interprétation vis-à-vis de la minimisation des couples articulaires semble plus facile. Le but est d'évaluer l'apport d'un modèle musculosquelettique par rapport à un modèle corps rigide classique pour la simulation de posture.Le modèle musculo-squelettique utilisé pour cette étude a été développé à l'IFSTTAR par[START_REF] Fraysse | Estimation des activités musculaires au cours du mouvement en vue d'applications ergonomiques[END_REF] au cours de sa thèse.

  Figure 77: de percep expérimen minimisat

  Cette thèse a été réalisée dans le contexte du développement des modèles numériques biomécaniques de l'homme pour l'évaluation ergonomique des produits. Les questions de recherche de l'étude se sont focalisées en particulier sur deux points :Comment proposer des indicateurs objectifs de la gêne ressentie pour aider les ingénieurs dans leur processus de sélection du meilleur design de produit ? Comment prédire la posture d'un individu en tenant compte de l'effort exercé ? La définition d'indicateurs objectifs de l'inconfort à partir de paramètres biomécaniques n'est pas chose aisée. De nombreux paramètres biomécaniques peuvent être pris en considération. Dans cette étude, nous avons fait l'hypothèse qu'un meilleur confort d'utilisation pouvait être obtenu quand les gens sont à même de faire leurs propres réglages. Un modèle d'inconfort basé sur le concept du mouvement moins contraint a été proposé et illustré par un cas d'étude sur le mouvement de débrayage. Les paramètres pertinents ont été identifiés en comparant des configurations imposées et moins contraintes. Bien que l'approche proposée soit générique, les indicateurs d'inconfort identifiés sont spécifiques à la tâche et dépendants des conditions d'essai. En effet, comme seule la position de la pédale a été considérée comme paramètre ajustable, les indicateurs proposés peuvent ne pas être applicable à d'autres situations dans lesquelles d'autres paramètres de conception seraient ajustables. Mais la manière de simuler des mouvements moins contraints reste en question. En effet, les utilisateurs finaux pourraient être intéressés à prédire les moins contraint de configuration. De notre point de vue, une approche basée sur des données telle que celle mis en oeuvre dans RPx pourrait être proposé pour prédire de tels mouvements. Il reste que comme pour toutes les méthodes basées sur des données, les résultats dépendent fortement de l'ensemble de données sous-jacent et il sera difficile d'extrapoler en dehors des conditions expérimentales. Ensuite, une question est de savoir comment combiner les indicateurs pour obtenir une évaluation globale d'un produit. Un score global est généralement préféré par les ingénieurs car il permet de différencier rapidement plusieurs alternatives de conception. Nos indicateurs d'inconfort proposés peuvent être efficaces pour comparer des propositions de design, et ne répondent donc pas complètement aux attentes des industriels en matière d'évaluation ergonomique absolue. De notre point de vue, notes globales et indicateurs ne sont pas fondamentalement opposés les uns aux autres, mais doivent être considérés comme des outils complémentaires pour l'évaluation ergonomique de produits. Ainsi ces indicateurs pourraient fournir des éléments objectifs pour un expert pour former une méthode d'évaluation globale d'un design de produit. Enfin, les indicateurs de gêne proposés sont basés sur les paramètres cinématiques et dynamiques d'un mouvement expérimental, ce qui implique que la simulation de mouvement utilisant un mannequin numérique doit être validée expérimentalement aussi bien du point de vue de la cinématique que de la dynamique. Ceci est particulièrement important pour les paramètres dynamiques tels que les couples articulaires car leur calcul dépend fortement du contrôle de la direction de la force. C'est sur ce point en particulier que les deux parties de cette thèse se relient. Dans une première étape visant à améliorer les méthodes de prédiction de posture impliquant la production d'un effort, le mécanisme de production d'effort sur une commande automobile a été étudié à travers l'exemple de la pédale d'embrayage. En utilisant des données expérimentales et de la simulation biomécanique, deux aspects de la production d'effort ont été étudiés: le contrôle de la direction d'effort et le contrôle de la posture.Un des principaux résultats de cette étude est que la direction de la force appliquée sur la pédale ainsi que l'ajustement postural sont principalement expliqués par la nécessité de réduire le chargement articulaire lorsque l'on augmente le niveau de force demandé. Par ailleurs, l'utilisation d'un modèle musculo-squelettique avec un critère d'optimisation minimisant l'activité musculaire a permis d'améliorer la prédiction de la direction d'effort pour des niveaux de force de faible intensité par rapport à un critère de minimisation des couples articulaires et à un modèle multi-corps sans muscles de l'homme. Néanmoins, aucune des approches de simulation proposées n'a permis d'expliquer la déviation latérale de l'effort sur la pédale, ce qui suggère que la direction de la force latérale peut être contrôlée par d'autres mécanismes. L'étape suivante de cette étude serait donc de mettre en place une méthode permettant de prédire simultanément la direction d'effort et l'ajustement postural. L'étude tendant à montrer que la réduction du chargement articulaire explique à la fois le contrôle de la direction et celui de la posture, il pourrait donc être envisagé dans un premier temps de fusionner les deux algorithmes utilisés dans ce travail. Reste qu'une des principales difficultés de la simulation pour les commandes automobile est la gestion des contacts entre l'individu et le siège. Pour cela, une modélisation plus réaliste des contacts avec le siège est nécessaire.

  SUBJECTS ........................................................................................................... 2.2 EXPERIMENTAL SET UP ......................................................................................... 2.3 EXPERIMENTAL CONDITIONS .................................................................................. 2.4 EXPERIMENTAL PROCEDURE .................................................................................. 2.5 DATA PROCESSING ............................................................................................... 2.6 EVALUATION OF THE MOTION RECONSTRUCTION ...................................................... 2.7 SUMMARY OF PROCESSED DATA .............................................................................

	1	INTRODUCTION
	2	MATERIAL AND METHODS
		2.1
	3	ANALYSIS OF THE DISCOMFORT PERCEPTION
	3.1 QUESTIONNAIRE 6 LIMITATIONS
	7	CONCLUSION
	8	APPENDIX
		8.1 SELECTION 8.5 CHARACTERIZATION DATA OF EACH GROUP OF SUBJECTS IN DHERGO EXPERIMENT .........
		8.6	MARKERS' PLACEMENT ON SUBJECT FOR THE DHERGO

ANALYSIS .................................................................................... 3.2 DISCOMFORT RATINGS ANALYSIS ............................................................................ 4 MOTION ANALYSIS OF THE CLUTCH PEDAL OPERATION 4.1 COMPARISON OF CLUTCHING MOVEMENTS FOR IMPOSED AND LESS CONSTRAINED PEDAL CONFIGURATIONS ............................................................................................................. 4.2 ANALYSIS OF CLUTCH PEDAL FORCE EXERTION ........................................................... 5 DISCUSSION 5.1 PEDAL POSITION ADJUSTMENT AND DISCOMFORT PERCEPTION .................................... 5.2 PEDAL FORCE CONTROL AND DISCOMFORT PERCEPTION ............................................. 5.3 TOWARDS A PROPOSAL FOR DEFINING OBJECTIVE DISCOMFORT INDICATORS .................. OF THE TEST CLUTCH PEDAL CONFIGURATIONS ........................................... 8.2 MEASUREMENT CHAIN FOR DHERGO EXPERIMENT ................................................... 8.3 ANTHROPOMETRIC MEASUREMENTS ....................................................................... 8.4 SUBJECTS' ANTHROPOMETRIC DIMENSIONS FOR THE DHERGO EXPERIMENT .................. EXPERIMENT ............................ 8.7 MARKERS' PLACEMENT ON MOCK UP FOR THE DHERGO EXPERIMENT .......................... 8.8 DISCOMFORT QUESTIONNAIRE FOR THE DHERGO EXPERIMENT ................................... 8.9 EXPERIMENTAL SHEET OF THE DHERGO EXPERIMENT ................................................. 8.10 RAMSIS™ DIGITAL HUMAN MODEL

Table 1 : Main characteristics of each group of subjects. Means and standard deviations are given.

 1

		Young male Young female Older male	Older female All subjects
	N	5	5	5	5	20
	Age (years)	27 ± 5	26 ± 4	73 ± 5	69 ± 2	48 ± 23
	Stature (mm)	1773 ± 49	1667 ± 30	1724 ± 22	1599 ± 46	1691 ± 75
	Weight (kg)	70 ± 12	61 ± 3	81 ± 5	63 ± 6	69 ± 11

Table 2 )

 2 .

Table 2 :

 2

	: Definition of t
			Seat he
			(mm
	BMW W1	256
	BMW W2	247
	BMW W4	174
	PCA1 1	355
	PCA2 2	272
	REN3 3	360
	The con nfigurations
	investig gation space
	this last t configurat
	configu urations, rep
	subject	performed
	In orde er to limit
	controll lable by the
	paramet ter. Seat he
	provide	d by end-u
	Neverth heless betw

Table 3 : Training clutch pedal configurations

 3

		Seat height	Clutch Pedal center of rotation	CP Travel angle CP Travel length
		H30 (mm)	X (mm)	Y (mm)	Z (mm)	(°)	(mm)
	CAL1	200	-900	-87	147	30	170
	CAL2	400	-900	-87	147	0	170
	CAL3	300	-900	-87	147	15	140

Table 4 : Estimation of the contact force between left thigh and the seat F Thigh Seat and the resulting hip moment T Thigh Seat for tested pedal configuration in DHErgo experiment using the seat pressure map data.

 4

	(N)	(Nm)

Summary of processed data DHErgo experiment Number of participants 20 Subject characterization trials Anthropometry 41 ROM 7 Max. joint torque 19

	Clutch pedal trials	16 (clutch pedal operation)
	Excluded participant	0 (2 for dynamic analysis only)
	Number of reconstructed trials	16 x 20 = 320
	for motion analysis	(14 x 20 = 280 for dynamic analysis)

Table 5 : Assessment of the pedal position in x, y and z directions at the start and the end depression for imposed and less constrained configurations. Frequencies of the answers for each direction are shown.

 5

	Imposed Less Constrained Global

Table 7 : Means and standard deviations of the raw and centred CP50 scores according to the group of subjects (G), the test configuration (C) and the type of configuration (T)

 7

		CP50 raw scores	CP50 centred scores
	Older Female	14.2 ± 9.3
	Older Male	18.0 ± 10.5
	Younger Female	20.3 ± 8.6
	Younger Male	16.9 ± 9.0
	BMW1	17.8 ± 10.3	0.48 ± 8.3
	BMW2	16.8 ± 8.2	-0.55 ± 5.6
	BMW4	13.6 ± 11.0	-3.77 ± 7.9
	PCA1	20.7 ± 9.7	3.33 ± 8.1
	PCA2	16.1 ± 8.9	-1.21 ± 6.8
	REN3	21.5 ± 8.4	4.15 ± 8.9
	Imposed	19.7 ± 9.6	2.32 ± 7.7
	Less Constrained	15.0 ± 9.0	-2.32 ± 7.2
	All	17.3

C***, G***, T*** ± 9.6 0 C***, T*** ± 7.79 *p<0.05, **p<0.01, *** p<0.001, C: configuration, G: group of subjects, T: type of configuration

Table 8 : Means and standard deviations of the raw CP50 scores according to the perceived pedal resistance.

 8

		CP50 raw scores
	Very low	7.9 ± 12.1
	Low	11.4 ± 8.4
	Medium	18.3 ± 7.7
	High	24.7 ± 9.8
	All	17.3 ± 9.6***
	*p<0.05, **p<0.01, *** p<0.001

Table 9 : Means and standard deviations of the adjustments in x, y and z from an imposed pedal position to the preferred one for the six tested configurations

 9

		x (mm)	y (mm)	z (mm)
	Older Female	-1.9 ± 20.4	-21.2 ± 20.1	-21.1 ± 38.4
	Older Male	-5.6 ± 28.1	-17.7 ± 22.2	-18.5 ± 36.7
	Younger Female	-11.8 ± 13.8	-23.0 ± 20.8	-26.5 ± 30.1
	Younger Male	-8.8 ± 22.5	-22.1 ± 19.9	-1.2 ± 36.6
	BMW1	-3.0 ± 17.7	-41.0 ± 18.8	-45.5 ± 32.9
	BMW2	-3.4 ± 20.6	-4.2 ± 12.7	-46.6 ± 24.8
	BMW4	-1.4 ± 32.6	0.9 ± 4.0	-43.5 ± 30.8
	PCA1	-3.9 ± 24.8	-31.0 ± 22.6	-4.4 ± 25.7
	PCA2	-14.9 ± 18.0	-18.3 ± 14.0	0.2 ± 27.9
	REN3	-13.8 ± 23.1	-37.9 ± 16.3	4.6 ± 36.4
	Total	-8.8 C*, G* ± 22.5	-21.0 C*** ± 20.6	-16.8 C***, G** ± 36.5

*p<0.05, **p<0.01, *** p<0.001, C: configuration, G: Group of subjects

Table 10 : Mean values and standard deviations of force resultant, tangential force, normal and transversal force

 10

		Resultant (N) Tangential (N)	Normal (N)	Transversal (N)
	Older Female	188 ± 23	183 ±22	-30 ± 34	-8 ± 8
	Older Male	184 ± 23	179 ±22	-33 ± 29	-3 ± 9
	Younger Female	177 ± 31	167 ±25	-53 ± 33	-5 ± 9
	Younger Male	171 ± 19	158 ±16	-57 ± 32	-4 ± 10
	BMW1	183 ± 27	172 ±23	-56 ± 34	0 ± 9
	BMW2	174 ± 25	164 ±23	-53 ± 24	-6 ± 7
	BMW4	166 ± 19	160 ±18	-40 ± 24	-4 ± 7
	PCA1	176 ± 28	171 ±25	-14 ± 37	-12 ± 12
	PCA2	183 ± 25	172 ±24	-54 ± 32	-2 ± 8
	REN3	185 ± 25	179 ±22	-36 ± 29	-8 ± 8
	Imposed	181 ± 24	172 ±23	-45 ± 31	-2 ± 9
	Less Constrained	178 ± 26	168 ±24	-45 ± 36	-8 ± 8
	Total

Table 11 : Means and standard deviations of , and .

 11

Table 12 : Means and standard deviations of knee and ankle flexion/extension joint torque ([+] flexion and [-] extension)

 12

		Knee (Nm)	Ankle (Nm)
	Older Female	-24.5 ± 12.9	-8.9 ± 5.1
	Older Male	-28.7 ± 11.1	-12.1 ± 4.6
	Younger Female	-13.1 ± 11.7	-8.7 ± 5.6
	Younger Male	-12.8 ± 9.1	-14.8 ± 5.3
	BMW1	-18 ± 13	-13.3 ± 6
	BMW2	-17.1 ± 9.6	-12.4 ± 5.1
	BMW4	-12 ± 11.4	-12.5 ± 3.6
	PCA1	-25.4 ± 14	-7.4 ± 5.7
	PCA2	-17.6 ± 13	-12.2 ± 5.6
	REN3	-27.4 ± 12.2	-9 ± 6
	Imposed	-19 ± 12.9	-12 ± 5.2
	Less Constrained	-19.2 ± 13.4	-10.8 ± 6.2
	Total	-19.

Table 13 : Means and standard deviations of range of motion (ROM) in flexion/extension (hip, knee and ankle)

 13

	All subjects

Table 14 : Coefficients of correlation between CP50 raw scores, normal and tangential pedal forces, pedal force direction in the XZ plane ( ) and knee and ankle joint torques.

 14

		Normal	Tangential		Knee	Ankle	CP50 raw
		force	Force		torque	torque	scores
	Normal force	1	-0.1327*	0.8286***	-0.7848***	0.7137***	0.0723
	Tangential force		1	0.093	-0.236***	-0.2493***	-0.0611
				1	-0.8364***	0.6541***	-0.0062
	Knee torque				1	-0.5564***	-0.1489*
	Ankle torque					1	0.1205*
	CP50 raw scores						1

*p<0.05, **p<0.01, *** p<0.001.

Table 15 : Table of coefficients of correlation between CP50 raw scores, CP50 centered scores and the proposed discomfort indicators. The significant coefficients of correlation

 15

		Ind	Ind	Ind	Ind	Ind	Ind	Ind	Ind	Ind	CP50	CP50
		HipStart	HipEnd	KneeStart	KneeEnd	AnkleStart	AnkleEnd	KneeJT	AnkleJT	LPos	Raw	Cent.
	IndHipStart	1,000	0,872	0,235	0,277	0,088	0,305	-0,012 -0,354	0,086 -0,038 0,090
	IndHipEnd		1,000	0,249	0,456	-0,204	0,393	-0,223 -0,296	0,145 -0,222 0,029
	IndKneeStart			1,000	0,725	-0,084	0,589	-0,123 -0,411	0,004 -0,046 -0,029
	IndKneeEnd				1,000	-0,290	0,641	-0,229 -0,344	0,019 -0,258 -0,091
	IndAnkleStart					1,000	-0,402	0,249	0,045	-0,112 0,281 0,058
	IndAnkleEnd						1,000	-0,127 -0,601	0,083 -0,064 -0,025
	IndKneeJT							1,000	-0,080	0,004 0,215 0,079
	IndAnkleJT								1,000	-0,137 -0,115 -0,060
	IndLPos									1,000 0,063 0,204
	CP50 Raw										1,000 0,809
	CP50 Cent.											1,000

(i.e. p value < 0.05) are noted in red.

Table 16 : Eleven initial clutch pedal configurations provided by end users in DHErgo project for the first case study.

 16

		Seat Height	Travel Length	Travel Angle	Clutch pedal initial position
		(mm)	(mm)	(in degree)	CPx (mm) CPy (mm) CPz (mm)
	BMW1	256	131	0	-814.5	-60	-69
	BMW2	247	163	0	-831	-110	-64
	BMW3	314	151	8	-777	-120	-136
	BMW4	174	149	0	-816	-120	17
	BMW5	253	150	0	-816	-100	-77
	PCA1	355	161	23	-770	-70	-199
	PCA2	272	157	8	-766	-80	-130
	PCA3	254	158	15	-799	-90	-96
	REN1	240	139	8	-799	-70	-68
	REN2	300	139	8	-776	-70	-140
	REN3	360	137	15	-761	-70	-218
	All	275 ± 54	149 ± 11	8 ± 7.5	793 ± 24	-87 ± 22	-107 ± 67

Table 18 :

 18

	01_SD	1684 145 216 394 300 345 975 90 234 665 252 231 592 592 428 322 358 220 1276 196 446 591 500
	02_LZ	1699 146 209 421 327 354 990 91 250 731 288 245 621 621 440 376 392 233 1303 203 448 613 512
	03_FD	1813 148 233 478 349 362 1006 98 278 813 310 280 623 623 426 556 395 234 1413 201 502 595 523
	04_VM 1639 149 216 421 298 364 1061 91 245 738 270 246 641 641 404 339 360 209 1264 221 429 607 489
	05_SL	1756 147 232 437 340 297 871 94 255 697 272 252 516 516 346 302 325 202 1328 172 457 588 514
	06_MR 1700 171 225 436 343 306 866 93 252 713 256 250 490 490 371 314 343 221 1270 210 478 590 498
	07_YP	1775 147 225 467 358 362 1055 97 272 814 307 281 626 626 431 385 412 247 1372 208 480 617 522
	08_ZS	1682 147 206 410 314 339 945 91 242 671 246 225 551 551 379 321 356 216 1283 200 436 571 495
	09_HB 1823 163 224 479 349 340 1020 98 283 909 298 276 611 611 418 348 383 242 1391 229 501 509 537
	10_VH 1632 150 205 409 291 334 958 89 234 728 283 248 625 625 425 359 386 242 1232 200 434 578 483
	11_JF	1708 168 228 475 345 373 1039 101 262 938 291 276 583 583 450 368 376 238 1302 212 484 581 543
	12_MC 1610 154 209 417 304 347 961 93 236 886 263 238 554 554 388 323 337 210 1211 230 448 585 495
	13_AT 1730 156 229 442 348 342 1000 95 259 1032 306 273 572 572 396 352 363 235 1299 270 468 632 541
	14_AF	1630 149 211 420 335 356 1019 89 243 777 282 245 609 609 421 345 348 216 1221 255 435 582 480
	15_PP	1695 168 220 441 368 357 1000 100 262 1064 284 279 561 561 435 372 375 237 1268 280 472 628 555
	16_ED 1530 159 219 423 285 335 1010 90 236 876 272 245 571 571 411 319 345 205 1165 290 412 538 446
	17_AG 1743 166 212 500 369 372 1029 95 273 1019 309 286 581 581 415 352 401 245 1302 250 485 615 533
	18_AC 1645 152 210 420 329 377 1085 88 232 932 268 240 612 612 438 351 358 224 1243 213 456 612 505
	19_JL	1745 169 239 484 360 360 1032 96 262 996 319 294 578 578 423 372 406 249 1319 275 474 619 533
	20_DN 1580 148 207 404 275 336 998 87 237 845 259 231 530 530 339 299 327 203 1195 210 417 534 470
		Young subjects
		Older subjects

of each gro of the motion t for each group

		oup
		Young
		-13
		87 ±
		37 ±
		-14
	ot.	-30
	t.	31 ±
		9 ±
		133
	ot.	-38
	t.	17 ±
	ion	46 ±
	on	121
		-34 ±
		12 ±
		106 ±
		112 ±
		105 ±
		49 ±
	ion	91 ±
	on	34 ±

of subj tested in the ch of subjects. M

		jects in DHE
		haracterization
		eans and stand
	g male Youn ng female O
	± 9	-2 22 ± 5
	± 5	9 91 ± 3
	± 4	5 50 ± 2
	± 3	-1 18 ± 4
	± 5	-3 32 ± 6
	± 14	4 40 ± 6
	± 6	1 12 ± 6
	± 6	13 38 ± 5
	± 7	-5 54 ± 13
	± 12	2 22 ± 7
	± 6	3 39 ± 8
	± 7	12 24 ± 5
	± 13	-4 47 ± 4
	± 7	1 17 ± 5
	± 30	6 67 ± 9
	± 17	8 83 ± 8
	± 17	69 9 ± 23
	± 19	3 34 ± 9
	± 32	8 8 ± 33
	± 4	1 18 ± 4

Ergo experi of the subject dard deviations

	Older male
	-6 ± 12
	88 ± 27
	25 ± 15
	-16 ± 6
	-46 ± 24
	22 ± 9
	13 ± 7
	122 ± 7
	-40 ± 6
	24 ± 13
	56 ± 3
	123 ± 4
	-39 ± 11
	1 ± 12
	91 ± 23
	98 ± 19
	75 ± 9
	36 ± 8
	55 ± 14
	36 ± 9

Ch iment ts' ROM. s are presented

		2	hapter 2
	8.6	M Markers' p
			d.
	LFHDM LBHDM	Older female e All subjec cts
	MCV7M	-18 ± 4	-15 ± 10 0
	MSJNM	81 ± 11	87 ±
	LIASMK	40 ± 4	37 ±
	LIPSMK	-20 ± 6 -28 ± 6	-17 ± -34 ± 14 4
	LICTMK LFLEMK LTH1MK LTH3MK LFALMK LSH1MK LSH3MK LFCCM LFM2M	25 ± 9 14 ± 8 125 ± 5 -43 ± 6 22 ± 12 44 ± 2 119 ± 6 -41 ± 12	29 ± 12 ± 7 130 ± -44 ± 10 0 21 ± 46 ± 8 122 ± -40 ± 11
	LSHOM	14 ± 8	11 ±
	LELBMK	49 ± 17	78 ±
	LHANM	71 ± 11	91 ±
	RKNEM	66 ± 12	79 ±
			22 ± 9	35 ±
			49 ± 38	71 ±
			22 ± 10	27 ±
				83

M Markers' placement o on mock up p for the DH

	8.7	lacement o on subject fo for the DHE rgo experim ment HErgo exper riment
				Descrip
			N N° Name	(orient
				driver'
			1	FRA_BL L	Back le
			2	FRA_BR R	Front l
			3	FRA_FL L	Back r
			4	FRA_FR R	Front r
			5	SBA_BL L	Back le
			6	SBA_BR R	Front l
			7	SBA_FL L	Back r
			8	SBA_FR R	Front r
			9	PHX_BK K	H-poin
			1 0 PHX_FT T	H-poin
			1 1 SEA_BL L	Back le
			1 2 SEA_BR R	Front l
			1 3 SEA_BU U	Up left
			1 4 SEA_FL L	Back r
			1 5 SEA_FR R	Front r
			1 6 SEA_FU U	Up righ
			1 7 FLO_BL L	Back le (horizo
			1 8 FLO_BR R	Front l (inclin
			1 9 FLO_BM M	Middle (horizo
			2 0 FLO_FL L	Back r (horizo
			2 1 FLO_FR R	Front r (inclin
		Left temple Left back head th cervical vert ugular Notch ( Left anterior su Left posterior s Left iliac crest Left femoral la Thigh technica Thigh technica Left lateral mal hank technica hank technica Left foot calcan tebrae (Sternum) uperior iliac sp superior iliac s tubercule ateral epicondy l cluster mark l cluster mark lleolus al cluster mark al cluster mark neous pine spine yle ker 1 LTH2 RFHD RBHD MSX DMK DMK SMK 2 2 FLO_FM M 2 3 CPD_BU U 2 4 CPD_FU U RIAS RIPS RICT LFM ker 3 LTH4 LTAM ker 1 LSH2 SMK SMK TMK EMK 2MK 4MK MMK 2MK 2 5 CPD_YB B 2 6 CPD_YF F 2 7 CPD_YM M 2 8 CPD_XP P 2 9 CPD_XM M 3 0 CPD_ZP P 3 1 GPD_UP P 3 2 GPD_DN N 3 3 WHE_U UW Right temp Right back Xiphoid pro ple Middle (horizo k head rocess (Sternu Centre m) Centre Right anter Right poste rior superior il erior superior Y-axis liac spine Y-axis iliac Y-axis spine Right iliac Left femora Thigh techn Thigh techn Left medial Shank tech crest tubercul al medial epic nical cluster m nical cluster m X-axis le X-axis condyle Z-axis marker 2 Accele marker 4 Accele al malleolus hnical cluster m Steerin marker 2 3 4 WHE_M MW Steerin ker 3 LFM 1MK Left foot 1 s st metatarsus 3 5 WHE_D W Steerin
		Left foot 2 nd m etatarsus	LFM 5MK	Left foot 5 t th metatarsus
		Left shoulder	RSHO OMK	Right shoul ulder
		Left elbow	RELB BMK	Right elbow w
		Left hand	RHA NMK	Right hand d
		Right external k knee	RFOO OMK	Right foot
		Numb ber of marker rs		36

Range of Motion 20 xx_XX_H_FE_ROM_1 Hip Flexion-Extension ROM 21 xx_XX_H_AA_ROM_1 Hip Abduction-Adduction ROM 22 xx_XX_H_AR_ROM_1 Hip Axial Rotation ROM 23 xx_XX_K_FE_ROM_1 Knee Flexion-Extension ROM 24 xx_XX_K_AR_ROM_1 Knee Axial Rotation ROM 25 xx_XX_A_FE_ROM_1 Ankle Flexion-Extension ROM 26 xx_XX_A_AA_ROM_1 Ankle Abduction-Adduction ROM N° trial Filename Joint Angle Direction Force Level Comments Effort Value % Difference

		8.9 8.11 Questionnaire frequency tables Experimental sheet of the DHErgo experiment
	Joint Torque Measurement
	27	N° trial Filename xx_XX_H060_F_MX_1 Hip	90°	Configuration Flexion	Maximum	Comments
	28	Calibration plate xx_XX_H060_F_MX_2 Hip	90°	Flexion	Maximum
	29	1 xx_XX_H060_REST_1 CALIB_TAR1_1 Hip	90°	Target 1 Extension Rest
	30	2 xx_XX_H060_E_MX_1 Hip CALIB_TAR2_1	90°	Target 2 Extension Maximum
	31	3 xx_XX_H090_E_MX_2 Hip CALIB_TAR3_1	90°	Target 3 Extension Maximum
	32	Palpation xx_XX_K045_REST_1	Knee	45°	Flexion	Rest
	33	4 xx_XX_K045_F_MX_1 Knee xx_XX_LIAS_1	45°	Left Anterior Superior Iliac Spine (LIAS) Flexion Maximum
	34	5 xx_XX_K045_F_MX_2 Knee xx_XX_RIAS_1	45°	Right Anterior Superior Iliac Spine (RIAS) Flexion Maximum
	35	6 xx_XX_K045_E_MX_1 Knee xx_XX_LIPS_1	45°	Left Posterior Superior Iliac Spine (LIPS) Extension Maximum
	36	7 xx_XX_K045_E_MX_2 Knee xx_XX_RIPS_1	45°	Right Posterior Superior Iliac Spine (RIPS) Extension Maximum
	37	8 xx_XX_A090_F_MX_1 Ankle 90° xx_XX_LICT_1	Left Ilium Crest Tubercle (LICT) Flexion Maximum
	38	9 xx_XX_A090_F_MX_2 Ankle 90° xx_XX_RICT_1	Right Ilium Crest Tubercle (RICT) Flexion Maximum
	39	10 xx_XX_A090_REST_1 xx_XX_LFTC_1 Ankle 90°	Left Greater Trochanter (FTC) Extension Rest
	40	11 xx_XX_A090_E_MX_1 Ankle 90° xx_XX_LFME_1	Left Medial Femoral Epicondyles (FME) Extension Maximum
	41	12 xx_XX_A090_E_MX_2 Ankle 90° xx_XX_LFLE_1	Left Lateral Femoral Epicondyles (FLE) Extension Maximum
			13	xx_XX_LTTC_1		Left Tibial Anterior Tuberosity (TTC)
		14 15 N° Trial Filename xx_XX_LTAM_1 xx_XX_LFAL_1		Left Medial Malleoli (TAM) Left Lateral Malleoli (FAL) Configuration TravelLength	Discomfort Effort ratings ratings	Comments
		16 Discomfort perception xx_XX_LFNE_1		Left Fibula's Neck (FNE)
		42	Static Posture			CAL1		170
		43	17	xx_XX_STND_1		Static Standing CAL2 170
		44	18	xx_XX_SEAT_1		Static Seated CAL3	140
		45	Movement xx_XX_BMW1_MOTION1 BMW1		131
		46	19	xx_XX_HPCR_1 xx_XX_BMW1_NTMVT01 BMW1 Hip Circumduction 131
		47		xx_XX_BMW2_MOTION1 BMW2		163
		48		xx_XX_BMW2_ NTMVT01 BMW2		163
		49		xx_XX_BMW4_MOTION1 BMW4		149
		50 51		faible xx_XX_BMW4_ NTMVT01 BMW4 xx_XX_PCA1_MOTION1 PCA1	Faible	149 161	Moyen	Elevé	Très élevé	Extrêmement élevé
		52		xx_XX_PCA1_ NTMVT01 PCA1		161
		53		xx_XX_PCA2_MOTION1	PCA2		157
		54		xx_XX_PCA2_ NTMVT01 PCA2		157
		55		xx_XX_REN3_MOTION1 REN3		137
		56		xx_XX_REN3_ NTMVT01 REN3		137

Table 19 : Frequency table for Question 1

 19

		Q1 G**
		Yes (%)	No (%)
	Older Female	10.00	90.00
	Older Male	2.50	97.50
	Younger Female	11.25	88.75
	Younger Male	0.00	100.00
	BMW1	5.00	95.00
	BMW2	7.50	92.50
	BMW4	12.50	87.50
	PCA1	10.00	90.00
	PCA2	1.67	98.33
	REN3	7.50	92.50
	Imposed	6.88	93.13
	Less Constrained	5.00	95.00
	Total	5.94	94.06

Chi-square test significance: * p-value<0.05, ** p-value<0.01, *** p-value<0.001 G: Group of subjects, C: Configuration, T: Type of configuration

Table 20 : Frequency table for Question 2

 20

		Q2H G***, T***			Q2D T***			Q2P G**, C*, T***
		Too high	Good	Too low	Too far	Good	Too close	Too leftward	Good	Too rightward
		(%)	(%)	(%)	(%)	(%)	(%)	(%)	(%)	(%)
	Older Female	48.75	48.75	2.50	6.25	62.50	31.25	0.00	51.90	48.10
	Older Male	23.75	72.50	3.75	3.75	76.25	20.00	0.00	80.00	20.00
	Younger Female	40.00	57.50	2.50	7.50	70.00	22.50	0.00	55.00	45.00
	Younger Male	16.25	75.00	8.75	6.25	72.50	21.25	1.27	67.09	31.65
	BMW1	42.50	57.50	0.00	12.50	57.50	30.00	0.00	52.50	47.50
	BMW2	40.00	57.50	2.50	7.50	75.00	17.50	0.00	80.00	20.00
	BMW4	40.00	60.00	0.00	2.50	67.50	30.00	2.50	75.00	22.50
	PCA1	27.50	62.50	10.00	10.00	72.50	17.50	0.00	55.00	45.00
	PCA2	28.33	65.83	5.83	1.67	73.33	25.00	0.00	62.71	37.29
	REN3	22.50	72.50	5.00	10.00	70.00	20.00	0.00	57.50	42.50
	Imposed	55.63	36.25	8.13	11.25	48.75	40.00	0.63	35.63	63.75
	Less Constrained	8.75	90.63	0.63	0.63	91.88	7.50	0.00	91.77	8.23
	Total	32.19	63.44	4.38	5.94	70.31	23.75	0.31	63.52	36.16
			Chi-square test significance: * p-value<0.05, ** p-value<0.01, *** p-value<0.001
			G: Group of subjects, C: Configuration, T: Type of configuration

Table 21 : Frequency table for Question 3

 21

		Q3H G*, C***, T***		Q3D C*, T***			Q3P C***, T*
		Too high	Good	Too low	Too far	Good	Too close	Too leftward	Good	Too rightward
		(%)	(%)	(%)	(%)	(%)	(%)	(%)	(%)	(%)
	Older Female	10.13	62.03	27.85	39.24	59.49	1.27	0.00	51.25	48.75
	Older Male	11.39	77.22	11.39	28.75	62.50	8.75	0.00	85.00	15.00
	Younger Female	16.25	60.00	23.75	45.00	47.50	7.50	0.00	63.75	36.25
	Younger Male	8.75	77.50	13.75	37.97	59.49	2.53	1.27	69.62	29.11
	BMW1	22.50	72.50	5.00	30.00	65.00	5.00	0.00	55.00	45.00
	BMW2	22.50	65.00	12.50	50.00	45.00	5.00	0.00	82.50	17.50
	BMW4	15.00	75.00	10.00	25.00	72.50	2.50	2.50	85.00	12.50
	PCA1	5.00	55.00	40.00	57.50	35.00	7.50	0.00	57.50	42.50
	PCA2	5.88	75.63	18.49	29.66	66.10	4.24	0.00	65.55	34.45
	REN3	10.26	58.97	30.77	50.00	42.50	7.50	0.00	62.50	37.50
	Imposed	18.87	64.78	16.35	32.70	57.23	10.06	0.63	43.75	55.63
	Less Constrained	4.40	73.58	22.01	42.77	57.23	0.00	0.00	91.19	8.81
	Total	11.64	69.18	19.18	37.74	57.23	5.03	0.31	67.40	32.29

Chi-square test significance: * p-value<0.05, ** p-value<0.01, *** p-value<0.001 G: Group of subjects, C: Configuration, T: Type of configuration

Table 22 : Frequency table for Question 4

 22

			Q4 G*, T*
		Too long (%)	Good (%)	Too short (%)
	Older Female	56.25	42.50	1.25
	Older Male	47.50	50.00	2.50
	Younger Female	45.00	45.00	10.00
	Younger Male	46.25	53.75	0.00
	BMW1	45.00	52.50	2.50
	BMW2	62.50	32.50	5.00
	BMW4	30.00	67.50	2.50
	PCA1	50.00	45.00	5.00
	PCA2	50.00	47.50	2.50
	REN3	52.50	42.50	5.00
	Imposed	43.75	50.63	5.63
	Less Constrained	53.75	45.00	1.25
	Total	48.75	47.81	3.44

Chi-square test significance: * p-value<0.05, ** p-value<0.01, *** p-value<0.001 G: Group of subjects, C: Configuration, T: Type of configuration

Table 23 : Frequency table for Question 5

 23

			Q5 G**
		Little inclined (%)	Good (%)	Too inclined (%)
	Older Female	6.25	72.50	21.25
	Older Male	18.75	72.50	8.75
	Younger Female	5.00	66.25	28.75
	Younger Male	10.00	77.50	12.50
	BMW1	10.00	77.50	12.50
	BMW2	17.50	65.00	17.50
	BMW4	10.00	75.00	15.00
	PCA1	10.00	62.50	27.50
	PCA2	6.67	79.17	14.17
	REN3	12.50	60.00	27.50
	Imposed	11.88	66.88	21.25
	Less Constrained	8.13	77.50	14.38
	Total	10.00	72.19	17.81

Chi-square test significance: * p-value<0.05, ** p-value<0.01, *** p-value<0.001 G: Group of subjects, C: Configuration, T: Type of configuration

Table 24 : Frequency table for Question 6

 24

			Q6 C***
		Very Low (%)	Low (%)	Medium (%)	High (%)
	Older Female	5.00	20.00	55.00	20.00
	Older Male	2.50	25.00	60.00	12.50
	Younger Female	1.25	25.00	51.25	22.50
	Younger Male	1.25	35.00	48.75	15.00
	BMW1	0.00	30.00	52.50	17.50
	BMW2	0.00	40.00	45.00	15.00
	BMW4	5.00	55.00	32.50	7.50
	PCA1	0.00	12.50	62.50	25.00
	PCA2	4.17	20.00	60.83	15.00
	REN3	2.50	12.50	55.00	30.00
	Imposed	2.50	21.88	54.38	21.25
	Less Constrained	2.50	30.63	53.13	13.75
	Total	2.50	26.25	53.75	17.50

Chi-square test significance: * p-value<0.05, ** p-value<0.01, *** p-value<0.001 G: Group of subjects, C: Configuration, T: Type of configuration 8.12 Questionnaire reproducibility

Table 25 : Frequency table of the conditions of the reproducibility for each subject and each question. For each subject, the reprodicibility was evaluated on all questions. For each question, it was estimated on all subjects.

 25

			Cond1 (%)	Cond2 (%)	Cond3 (%)
			i.e. 0 similar answer	i.e. 2 similar answers	i.e. 3 similar answers
		01_SD	0	50
		02_LZ	0	25
		03_FD	0	45
		04_VM	5	60
		05_SL	5	30
		06_MR	0	40
		07_YP	0	10
		08_ZS	0	25
		09_HB	0	30
	Subjects	10_VH 11_JF	0 0	40 25
		12_MC	0	40
		13_AT	0	80
		14_AF	5	25
		15_PP	0	30
		16_ED	5	40
		17_AG	0	25
		18_AC	0	30
		19_JL	20	35
		20_ID	0	50
		Q1	0	10
		Q2H	2,5	35	62,5
		Q2D	0	45
		Q2P	0	20
	Questions	Q3H Q3D	5 7,5	42,5 52,5	52,5
		Q3P	0	30
		Q4	2,5	42,5
		Q5	0	32,5	67,5
		Q6	2,5	57,5

13 Imposed versus less constrained joint angles

Table 26 : Means, standard deviations and the mean differences of the lower limb joint angles at beginning and end of pedal depression.

 26

	Beginning of pedal depression	End of pedal depression

Table 27 : Coefficients of the fitted model from linear regression

 27

		Coefficient value
	Constant	21.8***
	IndHipStart	26.5*
	IndHipEnd	-27.4**
	IndKneeStart	5.3
	IndKneeEnd	-6.5
	IndAnkleStart	3.4*
	IndAnkleEnd	3.1
	IndKneeJT	1.5
	IndAnkleJT	-11.1
	IndLPos	7.1
	R² = 20.2%
	Adjusted R² = 17.2%

Table 28 : Results of the regression model selection procedure

 28

	R²	Adjusted R²	Included variables
	20.0	17.7	ABDEFHI	A=IndHipStart
	19.6	17.6	ABDEHI	B=IndHipEnd
	20.1	17.5	ABCDEFHI	C=IndKneeStart
	20.2	17.2	ABCDEFGHI	D=IndKneeEnd
	18.7	17.1	ABDHI	E=IndAnkleStart
	17.9	16.6	ABDH	F=IndAnkleEnd
	16.6	15.6	ABH	G=IndKneeJT
	15.0	14.3	AB	H=IndAnkleJT
	7.9	7.5	E	I=IndLPos
	0.0	0.0

Table 29 : Linear discriminant functions F1, F2 and F3 for Model1 to Model8

 29

		Fonction Constant IndHipStart IndHipEnd IndKneeStart IndKneeEnd IndAnkleStart IndAnkleEnd IndKneeJT IndAnkleJT IndLPos
		F1	-61.7	-98.4	125.0	60.8	73.0	9.2	11.5	57.7	80.5	26.1
	Model1	F2	-45.8	-98.0	111.7	57.9	46.8	11.9	19.9	42.8	63.5	24.9
		F3	-49.5	-102.1	114.1	56.4	45.8	13.1	21.6	46.3	61.5	30.1
		F1	-51.8	-73.4	96.9	33.3	27.4	11.0	19.0	49.9	87.1	24.7
	Model2	F2	-44.0	-99.9	106.1	32.9	13.7	11.7	23.0	44.7	63.8	24.8
		F3	-45.3	-81.3	94.2	42.9	17.3	12.6	21.5	46.0	66.1	28.6
		F1	-60.9	-140.1	150.2	51.4	45.3	15.0	14.8	50.1	77.6	26.4
	Model3	F2	-52.2	-139.0	144.9	55.2	33.8	15.3	19.3	35.7	61.5	29.7
		F3	-51.8	-135.5	139.7	57.4	35.4	16.4	18.4	39.8	58.3	31.0
		F1	-49.3	-83.2	100.5	32.7	26.4	12.5	15.3	45.8	80.6	20.8
	Model4	F2	-43.7	-93.0	103.3	40.2	19.5	12.0	18.9	37.6	63.7	22.4
		F3	-42.1	-84.2	94.6	39.9	20.8	13.2	17.1	38.7	60.0	26.1
		F1	-53.6	-78.9	97.5	25.2	26.2	14.1	20.2	55.4	104.8	13.7
	Model5	F2	-43.4	-87.9	99.2	36.6	22.5	14.5	23.0	34.4	70.2	17.3
		F3	-42.0	-75.6	87.1	32.1	19.3	14.2	22.4	45.4	82.6	15.7
		F1	-48.7	-42.5	88.1	51.8	29.4	7.0	15.8	38.5	85.8	9.0
	Model6	F2	-38.7	-56.7	81.4	40.7	12.9	6.9	24.8	32.9	76.8	9.2
		F3	-39.7	-38.5	74.5	56.0	18.6	8.4	20.3	30.8	72.6	13.2
		F1	-45.8	-54.0	86.9	42.5	28.3	7.8	11.3	37.4	75.2	19.3
	Model7	F2	-43.4	-86.9	95.9	40.5	6.8	5.9	26.2	35.1	66.3	19.8
		F3	-38.4	-55.0	77.2	46.2	15.1	9.3	17.4	31.4	61.4	23.6
		F1	-45.2	-33.6	72.7	67.1	37.7	2.3	16.4	29.3	78.2	20.6
	Model8	F2	-42.7	-28.8	64.4	75.1	25.8	1.6	26.8	22.4	81.3	19.5
		F3	-39.0	-29.4	61.9	74.1	29.2	3.1	21.1	23.7	67.9	23.9

Table 30 : Linear discriminant functions F1, F2 and F3 for Model9 to Model10

 30

		Fonction Constant IndHipStart IndHipEnd IndKneeStart IndKneeEnd IndAnkleStart IndAnkleEnd IndKneeJT IndAnkleJT IndLPos
		F1	-46.9	-61.8	79.8	28.9	38.5	10.0	6.5	51.6	92.0	14.2
	Model9	F2	-30.4	-50.1	62.1	38.3	21.2	8.7	12.2	31.9	66.3	17.5
		F3	-35.7	-52.7	65.6	36.5	19.0	10.3	15.7	38.5	75.4	16.0
		F1	-48.6	-26.7	73.0	34.0	46.4	14.3	5.7	41.6	95.0	8.4
	Model10	F2	-33.6	-35.1	62.7	44.9	28.9	9.5	15.6	28.2	71.0	8.4
		F3	-37.0	-31.7	64.4	40.6	33.2	14.0	10.6	31.2	71.9	13.9

Table 31 : Global and discomfort category prediction rates of the LDA models on the selected samples of trials

 31

	Model1	Model2	Model3	Model4	Model5	Model6	Model7	Model8	Model9	Model10

Maximum pedal force, force perception and control of pedal force direction

  SUBJECTS ......................................................................................................... 2.2 EXPERIMENTAL SET UP ....................................................................................... 2.3 TEST CONDITIONS .............................................................................................. 2.4 EXPERIMENTAL PROCEDURE ................................................................................ 2.5 DATA PROCESSING ............................................................................................. 2.6 EVALUATION OF RECONSTRUCTED MOTIONS .......................................................... 2.7 SUMMARY OF PROCESSED DATA ........................................................................... MAXIMUM PEDAL FORCE EXERTION (EXPMAX) ....................................................... 3.2 INTERMEDIATE LEVELS OF PEDAL FORCE EXERTION AND FORCE PERCEPTION (EXPPCP) ...

	1	INTRODUCTION
	2	MATERIAL AND METHODS
		2.1
	3	EXPERIMENTAL OBSERVATIONS
		3.1
	4	DISCUSSION
	5	PEDAL FORCE CONTROL SIMULATION
	5.1 SIMULATION 6 CONCLUSION
	7	APPENDIX
		7.1 MEASUREMENT CHAIN FOR FAC 7.7 TANGENTIAL, NORMAL AND TRANSVERSAL PEDAL FORCE COMPONENTS ACCORDING TO THE
		FORCE LEVEL

OF THE PEDAL FORCE DIRECTION [SIM1] .............................................. 5.2 SIMULATION OF THE POSTURAL ADJUSTMENT [SIM2] .............................................. 5.3 DISCUSSIONS OF THE TWO SIMULATION METHODS ............................................... PROJECT .............................................................. 7.2 ANTHROPOMETRIC MEASUREMENTS ..................................................................... 7.3 SUBJECTS' ANTHROPOMETRIC DIMENSION FOR THE FAC PROJECT .............................. 7.4 MARKERS' PLACEMENT ON SUBJECT FOR THE FAC PROJECT ...................................... 7.5 MARKERS' PLACEMENT ON MOCK UP FOR THE FAC PROJECT .................................... 7.6 EXTRACT FROM EXPERIMENTAL SHEET OF THE FAC PROJECT .....................................

Table 32 : Main characteristics of the participants

 32

	Group	Age (years)	Stature (mm)	Weight (kg)
	Short females	30.8 ± 6.8	1582 ± 38	54.3 ± 10
	Average males	28.5 ± 8.7	1763 ± 40	73.1 ± 11.4
	Tall males	31.6 ± 7.2	1854 ± 29	82.4 ± 10.3

Table 33 : Categories of the Borg's CR10 for the perceived exertion with the corresponding ratio scalars.

 33

	Force level (Category)	Force level (Ratio)
	Very low	1
	Low	2
	Medium	3
	High	5
	Maximum	10

Table 34 : Means and standard deviations of the force resultants and the three components according to pedal position and group of subjects.

 34

		Resultant (N)	Normal (N)	Tangential (N)	Transversal (N)
	P1F	616 ± 269	-279 ± 150	540 ± 237	-49 ± 27
	P1M	779 ± 346	-218 ± 114	740 ± 336	-71 ± 40
	P2F	631 ± 296	-277 ± 155	560 ± 261	-55 ± 30
	P2M	749 ± 314	-185 ± 97	718 ± 305	-70 ± 38
	P3F	543 ± 256	-284 ± 159	455 ± 211	-44 ± 34
	P3M	677 ± 295	-176 ± 106	646 ± 281	-71 ± 40
	Tall men	773 ± 236	-287 ± 103	706 ± 242	-58 ± 37
	Average men	729 ± 322	-257 ± 163	669 ± 297	-72 ± 37
	Short women	446 ± 254	-145 ± 103	411 ± 244	-49 ± 32
	Total	666 G***, P* ± 304	-237 G***, P** ± 138	610 G***, P*** ± 289	-60 G**, P** ± 37
		*p<0.05, **p<0.01, *** p<0.001. G: group of subjects; P: pedal position

Table 35 : Means and standard deviations of , and .for maximum pedal force exertion.

 35

		(deg)	(deg)	(deg)
	P1F	-5.8 ± 7.5	4.7 ± 1.6	-1.1 ± 1
	P1M

Table 36 : Means and standard deviations of the pedal force resultant according to the force level for the three groups of subjects.

 36

			Force resultant (N)
		Short women	Average men	Tall men
	Very low	45 ± 21	98 ± 46	90 ± 32
	Low	77 ± 57	149 ± 82	141 ± 48
	Medium	110 ± 40	255 ± 135	273 ± 126
	High	244 ± 134	410 ± 290	462 ± 173
	Maximum	407 ± 217	754 ± 319	826 ± 224
	All	215 ± 204 F***	403 ± 349 F***	437 ± 339 F***
		*p<0.05, **p<0.01, *** p<0.001. F: force level
	The normalized pedal forces are shown in Table 37 and Figure 46. As expected, the

normalized force increased with the force level. No significant effect of the group of subjects was found on for each force level. It suggests that the pedal force perception law based on normalized forces was the same for all subjects.

Table 39 :

 39

	Globally
	pelvis b
	also be
	groups o
	on z-ax
	they als
	displace

xis (A.), y axis (B.) and z axis

Table 40 : Means and standard deviations of hip abduction/adduction angle, hip, knee and ankle flexion/extension angles for all subjects according to the force level

 40

		Hip Ab/Ad (deg.)	Hip F/E (deg.)	Knee F/E (deg.)	Ankle F/E (deg.)
	Rest	5.2 ± 5.7	58.3 ± 13.9	48 ± 8.3	65.7 ± 5.4
	Very Low	4.9 ± 5.5	57.9 ± 11.5	46.7 ± 8.4	67 ± 5.6
	Low	5.5 ± 5.6	55.9 ± 14.1	46 ± 8.7	65.4 ± 6.9
	Medium	5.6 ± 5	55.6 ± 11.9	43.5 ± 9.1	64.6 ± 5.9
	High	6.2 ± 5.3	52.9 ± 11.7	41.1 ± 9.6	63.4 ± 6.9
	Maximum	8.5 ± 6.3	48.8 ± 11.1	35.8 ± 10	62.7 ± 5.6

. Ab/Ad = Abduction/Adduction, F/E = Flexion/Extension.

Table 42 : Means and standard deviations of hip, knee and ankle flexion/extension angles for all subjects according to the force level in the simulation of postural adjustment (Sim2) and in the pedal force perception experiment (ExpPcp). F/E = Flexion/Extension.

 42

			ExpPcp (deg.)			Sim2 (deg.)
		Hip F/E	Knee F/E	Ankle F/E	Hip F/E	Knee F/E	Ankle F/E
	Very low	58 ± 12	47 ± 8	67 ± 6	58 ± 14	48 ± 8	66 ± 5
	Low	56 ± 14	46 ± 9	65 ± 7	58 ± 14	48 ± 8	66 ± 5
	Medium	56 ± 12	44 ± 9	65 ± 6	58 ± 14	47 ± 9	65 ± 5
	High	53 ± 12	41 ± 10	63 ± 7	56 ± 15	44 ± 11	65 ± 5
	Maximum	49 ± 11	36 ± 10	63 ± 6	53 ± 15	39 ± 11	63 ± 5

Table 44 : Means and standard deviations of the distance of the left knee to the vertical plane containing the HipP App axis for maximum pedal force exertion trials (a) and pedal force perception trials (b). Negative distance means that the knee was positioned leftward compared to the HipP App axis. Positive distance means that the knee was positioned rightward compared to the HipP App axis.

 44

	a)	Distance Knee/HipP App axis (mm)	b)	Distance Knee/HipP App axis (mm)
	P1F	-24,8 ± 29,3	Very low	-22,5 ± 25,7
	P1M	-40,5 ± 36,0	Low	-24,2 ± 25,9
	P2F	-24,9 ± 26,2	Medium	-24,5 ± 23,6
	P2M	-33,9 ± 31,9	High	-25,2 ± 26,7
	P3F	-26,8 ± 25,2	Maximum	-20,4 ± 23,1
	P3M	-31,4 ± 29,1	Short women	-36,9 ± 27,0
	Short women	-50,2 ± 28,3	Average men	-22,1 ± 16,8
	Average men	-29,0 ± 24,2	Tall men	-4,1 ± 13,0
	Tall men	-4,6 ± 13,2	Total	-22,9 ± 24,5
	Total	-30,4 ± 29,9

Bideltoid shoulder width Upper arm length Maximum Hip width M8 M9 M10 Foot breadth Foot length Minimum Waist circumference M11 M12 M14 Maximum Upper arm circumference Maximum Forearm circumference Maximum Thigh circumference M17 M19 M20 Maximum Lower leg circumference Sitting height Chest depth M21 v M22 M23 Forearm length Buttock-knee length Knee height sitting M24

	Chapter 3										r 3
	7.2 Subject M23 M24 M25 M29 M30 M36 Anthropometric measurements Markers' p	M37	Group	Seat adjustment Chosen Used	Laterality
	02_SM 480	420	57.4 1640 910	38	Female Small	-55	-55	Right-handed
	03_NC 482	405	46.3 1590 825	37	Female Small	-80	-80	Right-handed
	04_DD 565	425	80.4 1870 947	30	Male	Tall	85	85	Left-handed
	05_YP 534	M1 408	79.2 1799 942	20	Male	M2 Average	0	10	Right-handed	M3
	06_JC 535	410	71	1815 965	25	Male	Average	-20	0	Right-handed
	07_RZ 535	414	89.1 1860 951	35	Male	Tall	65	65	Right-handed
	08_CB 480	395	Stature 63.7 1684 925	38	Male	Average	-80	Head width -68	Right-handed	Head height
	09_RA 525	398	83	1779 902	33	Male	Average	-78	2.3	Right-handed
	10_LG 560	444	100.9 1908 998	36	Male	Tall	95	80	Left-handed
	11_GM 552	407	81.4 1848 928	34	Male	Tall	55	55	Right-handed
	12_SM 490	M4 396	52.1 1584 804	32	M5 Female Small	-80	-50	Right-handed	M6
	13_SN 570	400	81.2 1844 956	35	Male	Tall	80	60	Right-handed
	14_RT 570	430	70.6 1907 992	33	Male	Tall	50	50	Right-handed
	15_DM 568	419	67.7 1838 951	37	Male	Tall	40	40	Ambidextrous
	16_SR 532	419	54.9 1885 919	37	Male	Average	-20	-20	Right-handed
	17_AB 513	380	79.6 1630 832	42	Female Small	30	-10	Right-handed
	18_AA 542	423	572	1800 940	23	Male	Average	-50	-20	Right-handed
	19_AM 573	410	82.2 1880 944	43	Male	Tall	70	70	Left-handed
	20_EB 435	342	46.8 1545 842	26	Female Small	-80	-80	Left-handed
	21_GL 594	443	75.2 1884 941	24	Male	Tall	30	60	Right-handed
	22_FM 550 23_LT 518	424 M25 386	79.4 1843 979 Seat height Weight 85.9 1802 984	20 21	Male Male	Tall M29 Average	0 Stature with shoes on 20 Right-handed 20 20 Right-handed	M30	Trunk height (calculated)
	24_JT 687	M36 430	Age 98.1 1918 1000 21	Male	M37 Tall	60	Gender 93	Right-handed
	25_ED 558	430	70.7 1823 922	24	Male	Average	-40	20	Right-handed
	26_RP 510	380	83.5 1773 920	20	Male	Average	-40	-10	Right-handed
	27_RR 548	390	82.1 1774 925	44	Male	Average	-12	10	Left-handed
	28_LM 554	330	52.7 1537 834	23	Female Small	-80	-50	Left-handed
	29_CH 458	357	55	1618 880	32	Female Small	-80	-80	Right-handed
	30_CM 468	372	51.7 1636 878	22	Female Small	-80	-70	Right-handed
	31_NM 490	340	57.6 1640 894	31	Female Small	-80	-50	Right-handed
	142
	Small female
	Average male
	Tall male

N lacement o Number of m on subject fo markers for the FAC project

	7.5
	RHEA A Right tem mple
	LHEA A Left temp ple
	FHEA A	Forehead d middle
	C7T1	7 th cervic cal vertebrae
	USTR R	Upper ste ernum
	DSTR R	Lower ste ernum
	RACR R Right acr romio-clavicu lar joint
	LACR R Left acrom mio-clavicula ar joint
	RELT T	Right upp per arm techn ical marker
	LELT	Left uppe er arm technic cal marker
	RELE E	Right ext ternal elbow
	LELE	Left exter rnal elbow
	RWRT T Right low wer arm techn ical marker
	LWRT T Left lowe er arm technic cal marker
	RWRE E Right ext ternal wrist
	LWRE E Left exter rnal wrist
	RWRI I Right inte ernal wrist
	LWRI I	Left inter rnal wrist
	RHDE E Right ext ternal hand (ca arpus)
	LHDE E Left exter rnal hand (car rpus)
	RHDI	Right inte ernal hand (ca arpus)
	LHDI	Left inter rnal hand (carp pus)
	RASI	Right ant terior superior r iliac spine
	LASI	Left anter rior superior i iliac spine
	RKNT T Right thig gh technical m marker
	LKNT T Left thigh h technical ma arker
	RKNE E Right ext ternal knee
	LKNE E Left exter rnal knee
	RKNI	Right inte ernal knee
	LKNI	Left inter rnal knee
	RANT T Right sha ank technical m marker
	LANT T	Left shan nk technical m marker
	RANE E Right ext ternal malleolu us
	LANE E	Left exter rnal malleolus s
	RANI	Right inte ernal malleolu us
	LANI	Left inter rnal malleolus s
	RTOE E Right ext ternal tarsus
	LTOE E	Left exter rnal tarsus
	RTOI	Right inte ernal tarsus
	LTOI	Left inter rnal tarsus
	SEM1	Shoe (sol le) 1
	SEM2	Shoe (sol le) 2
	SEM3	Shoe (sol le) 3
	SEM4	Shoe (sol le) 4
		44

M Markers' placement o on mock up p for the FA

											C Chapter 3
	7.6	C project Extract from experimental sheet of the FAC project
	7.7	Tangential,
	N	Cmd	NomFichier	H	X	Y	Z	TypeEff DirEff	Description n NivEff	Note Commentaire
	Perception de l'effort I OOO xx_XX_TroneC Ii OOO xx_XX_EnvirD	N 1 2 N° Name 1 MAC1 2 MAC2 Environement LV1 P2F 286 Trone de Calibration	(orientation n according to o driver's poin nt of view) Front left o Back left o of mock-up fr of mock-up fra X ame ame X
	iii	OOO	xx_XX_PosRef	3 3 286 Pieds sur plancher et mains sur volant MAC3	Back right t of mock-up f frame X
	1	LV	xx_XX_L00_O1	4 4 286 -307 337 145 Stat	MAC4 O	Front right t of mock-up f frame Repos
	…	…	…	…	…	…	…	5 5 …	SIE1 …	Front right t of seat … …
	37	LV	xx_XX_LFA_L1	6 6 286 -307 337 145 Stat	SIE2 Gauche Faible Back right t of seat X
	38 39 40 41	PED PED PED PED	7 8 9 1 1 7 8 9 10 SIE6 SIE3 SIE4 SIE5 11 SIEH 300 -912 -70 -163 Stat O xx_XX_PMA_O1 300 -912 -70 -163 Stat xx_XX_P00_O1 O xx_XX_PMA_O2 300 -912 -70 -163 Stat O xx_XX_PFA_O1 300 -912 -70 -163 Stat O	Up right of Up left of s Back left o Front left o Reference f seat Repos seat Max X of seat Max X of seat H-point Faible X
	42	PED	xx_XX_PFO_O1	1 12 UPF1 300 -912 -70 -163 Stat O	Front left o of floor force Fort X	plate
	43	PED	xx_XX_PTF_O1	1 13 UPF2 300 -912 -70 -163 Stat O	Front right t of floor force e plate T Fable X
	44	PED	1 14 UPF3 xx_XX_PMO_O1 300 -912 -70 -163 Stat O	Back right t of floor force e plate Moyen X
	45	FAM	xx_XX_F00_O1	286 -130 350 75	1 15 UPF4 Stat O	Back left o of floor force p plate Repos
	…	…	…	…	…	…	…	1 16 PFS1 … …	Front left o of seat base … …
	51 Iv N	FAM OOO Cmd	xx_XX_FFA_O1 xx_XX_EnvirM NomFichier	1 1 1 2 17 PFS2 18 PFS3 19 PFS4 20 VOL1 Stat O Environement dernière config 286 -130 350 75 H X Y Z TypeEff DirEff	Front right Back right Back left o Steering w t of seat base Faible X t of seat base X of seat base wheel middle NivEff Note Commentaire
	Effort maximum statique					2 21 VOL2	Steering w wheel up
	52	FAM_A xx_XX_FA5_01	286 -40	2 22 VOL3 400 200 Stat O	Steering w wheel left Repos
	…	…	…	…	…	…	…	2 23 VOL4 … …	Steering w wheel right … …
	61	FAM_A xx_XX_FA3_M1	286 -220 400 -50	2 24 VOL5 Stat O	Steering co olumn Max X
	62	FAM	xx_XX_F12_01	2 25 CVO1 286 -220 300 200 Stat O	X-axis stee ering wheel se ensor (+) Repos
	… 81 82	… FAM PED	… xx_XX_F11_M1 xx_XX_P1F_01	2 2 2 2 26 CVO2 27 CVO3 28 CVO4 29 LVI1 … … Stat O 240 -936 -70 -79.5 Stat … … … … 286 -130 300 75 O	Y-axis stee X-axis stee Y-axis stee Y-axis gea ering wheel se ering wheel se ering wheel se ar stick sensor ensor (+) … … ensor (-) Max X ensor (-) r (-) Repos
	83	PED	xx_XX_P1F_M1	3 30 LVI2 240 -936 -70 -79.5 Stat O	Y-axis gea ar stick sensor r (+) Max X
	84	PED	xx_XX_P2F_01	3 31 LVI3 300 -912 -70 -163 Stat O	X-axis gea ar stick sensor r (+) Repos
	85	PED	xx_XX_P2F_M1	3 32 LVI4 300 -912 -70 -163 Stat O	Z-axis gear ar stick sensor Max X	(+)
	86	PED	xx_XX_P2M_01	3 33 PED1 300 -843 -70 -160 Stat O	Y-axis ped dal sensor (+) Repos
	87	PED	3 34 PED2 xx_XX_P2M_M1 300 -843 -70 -160 Stat O	Y-axis ped dal sensor (-) Max X
	88	PED	xx_XX_P3F_01	3 35 PED3 360 -891 -70 -253 Stat O	X-axis ped dal sensor (+) Repos
	89 90 91 92	PED PED PED PED	3 3 3 3 4 36 PED4 37 FMS1 38 FMS2 39 FMS3 40 FMS4 360 -891 -70 -253 Stat O 240 -867 -70 -83 Stat O xx_XX_P1M_M1 240 -867 -70 -83 xx_XX_P3F_M1 xx_XX_P1M_01 Stat O xx_XX_P3M_01 360 -824 -70 -246 Stat O	Z-axis ped Y-axis stat Y-axis stat Z-axis stati X-axis stat dal sensor (+) tic handbrakes tic handbrake tic handbrake tic handbrake Max X sensor (+) Repos sensor (-) Max X sensor (+) sensor (+) Repos
	93	PED	4 41 FMD1 xx_XX_P3M_M1 360 -824 -70 -246 Stat O	Y-axis dyn namic handbra ake sensor (+) ) Max X
	94	LV	xx_XX_L4O_01	4 42 FMD2 286 -212 396 119 Stat O	Y-axis dyn namic handbra ake sensor (-) Repos
	…	…	…	…	…	…	…	4 43 FMD3 … …	Z-axis dyn namic handbra ake sensor (+) … …
	121 LV	xx_XX_L2U_M1	4 44 FMD4 286 -410 268 218 Stat Haut	X-axis dyn namic handbra ake sensor (+) ) Max X
	Effort maximum Dynamique
	122 FAM	xx_XX_FS5_D1	286 -40	400 200 Dyna	O	Max	X
	123 FAM	xx_XX_FS2_D1	286 -220 400 200 Dyna	O	Max	X
	124 FAM_A xx_XX_FA2_D1	286 -220 400 200 Dyna	O	Max	X
	125 FAM_A xx_XX_FA5_D1	286 -40	400 200 Dyna	O	Max	X
	v	OOO	xx_XX_EnvirF	Environement dernière config			X
											147

normal and transversal pedal force components according to the force level Table 45: Means and standard deviations of the tangential component of the pedal force according to the force level. The results are presented for each group of subjects.

			Tangential force (N)
		Short women	Average men	Tall men
	Very low	38 ± 18	82 ± 41	71 ± 27
	Low	60 ± 39	130 ± 75	119 ± 44
	Medium	105 ± 43	227 ± 126	240 ± 117
	High	219 ± 118	372 ± 265	411 ± 162
	Maximum	363 ± 203	672 ± 283	728 ± 215
	All	191 ± 186	359 ± 313	383 ± 306

Table 46 : Means and standard deviations of the normal component of the pedal force according to the force level. The results are presented for each group of subjects.

 46

			Normal force (N)
		Short women	Average men	Tall men
	Very low	-22 ± 13	-52 ± 22	-54 ± 20
	Low	-31 ± 19	-71 ± 34	-75 ± 21
	Medium	-54 ± 23	-112 ± 52	-128 ± 52
	High	-104 ± 63	-169 ± 119	-205 ± 68
	Maximum	-174 ± 85	-326 ± 161	-378 ± 96
	All	-93 ± 84	-176 ± 155	-203 ± 149

Table 47 : Means and standard deviations of the transversal component of the pedal force according to the force level. The results are presented for each group of subjects.

 47

			Transversal force (N)
		Short women	Average men	Tall men
	Very low	-4 ± 2	-6 ± 5	-3 ± 5
	Low	-6 ± 6	-12 ± 9	-7 ± 6
	Medium	-10 ± 4	-19 ± 13	-18 ± 14
	High	-26 ± 15	-30 ± 20	-38 ± 25
	Maximum	-43 ± 26	-75 ± 30	-65 ± 36
	All	-22 ± 23	-36 ± 35	-33 ± 35

Variation of joint angles for short women, average men and tall men according to the force level

Table 51 : Means and standard deviations of hip abduction/adduction angle according to the group of subjects and the force level. Result presented are in degree.

 51

	Force level *

Table 52 : Means and standard deviations of hip flexion/extension angle according to the group of subjects and the force level. Result presented are in degree.

 52

	Force level ***

*p<0.05, **p<0.01, *** p<0.001

Table 53 : Means and standard deviations of knee flexion/extension angle according to the group of subjects and the force level. Result presented are in degree.

 53

	Force level ***

Table 54 : Means and standard deviations of ankle flexion/extension angle according to the group of subjects and the force level. Result presented are in degree.

 54

	Force level *

lexion/exte ons of hip, knee dal force exerti

	ension angl les accordin
	e and ankle flex xion/extension
		on
	exion/Extensi ion	Knee F Flexion/Exten
	(deg)		(deg)
	55 ± 9		44 ± 9
	63 ± 9		57 ± 7
	52 ± 10		45 ± 9
	61 ± 9		60 ± 5
	48 ± 11		47 ± 11
	56 ± 9		60 ± 8
	58 ± 11		37 ± 10
	66 ± 10		52 ± 12
	53 ± 11		37 ± 12
	63 ± 13		55 ± 12
	48 ± 12		40 ± 10
	58 ± 10		56 ± 10
	47 ± 12		23 ± 6
	59 ± 13		38 ± 8
	44 ± 13		29 ± 8
	54 ± 13		42 ± 8
	39 ± 14		33 ± 10
	48 ± 12		48 ± 9
	angle mean va	lues for each g

Table 59 : Means and standard deviations of simulated and experimental normal and transversal forces for a) maximum pedal force exertion and b) pedal force perception experiments.

 59 MODELING OF THE MUSCULOSKELETAL SYSTEM OF THE HUMAN BODY ........................ 2.2 MODELING OF THE PHYSIOLOGICAL BEHAVIOR OF THE MUSCLE .................................. 2.3 DEFINITION OF THE MUSCLE FORCE ....................................................................... 2.4 COMPUTATION OF THE MUSCLE FORCES ................................................................ DESCRIPTION OF THE MUSCULOSKELETAL MODEL USED ............................................ 3.2 OPTIMIZATION PROBLEM .................................................................................... FORCE FORCE DIRECTION .................................................................................... 4.2 MUSCLE FORCE PATTERNS ...................................................................................

	a)	Normal force (N)	Transversal force (N)
		Simulation	Experiment	Simulation	Experiment
	P1F	-260 ± 119	-279 ± 150	16 ± 23	-49 ± 27
	P1M	-166 ± 82	-218 ± 114	34 ± 32	-71 ± 40
	P2F	-254 ± 123	-277 ± 155	20 ± 22	-55 ± 30
	P2M	-141 ± 72	-185 ± 97	28 ± 27	-70 ± 38
	P3F	-261 ± 133	-284 ± 159	20 ± 18	-44 ± 34
	P3M	-136 ± 79	-176 ± 106	27 ± 26	-71 ± 40
	Short women	-125 ± 87	-145 ± 103	8 ± 16	-49 ± 32
	Average men	-232 ± 124	-257 ± 163	20 ± 24	-72 ± 37
	Tall men	-236 ± 105	-287 ± 103	40 ± 24	-58 ± 37
	All	-203 G***, P*** ± 117	-237 G***, P** ± 138	24 G*** ± 25	-60 G**, P** ± 37
	b)	Normal force (N)	Transversal force (N)
		Simulation	Experiment	Simulation	Experiment
	Very low	-38 ± 27	-44 ± 23	-3 ± 6	-4 ± 5
	Low	-57 ± 35	-63 ± 32	1 ± 7	-8 ± 7
	Medium	-93 ± 58	-100 ± 55	5 ± 12	-16 ± 12
	High	-156 ± 86	-167 ± 93	13 ± 16	-32 ± 21
	Maximum	-266 ± 123	-303 ± 144	30 ± 27	-62 ± 34
	Short women	-81 ± 86	-93 ± 85	3 ± 11	-22 ± 23
	Average men	-170 ± 134	-176 ± 155	8 ± 16	-36 ± 35
	Tall men	-173 ± 126	-203 ± 149	23 ± 28	-33 ± 35
	All	-146 G***, F*** ± 125 -163 G***, F*** ± 143 13 G***, F*** ± 22	-31 ± 32 G*, F***
		*p<0.05, **p<0.01, *** p<0.001. G: group of subjects; P: Pedal position; F: force level

OF SIMULATED AND EXPERIMENTAL PEDAL FORCE DIRECTIONS ............... 6.2 COMPARISON OF SIMULATED AND EXPERIMENTAL NORMAL AND TRANSVERSAL FORCES .

Table 60 : Lever arm values for the main hip adductors and abductors of the selected subject for mid travel and end travel pedal position. The values are in mm.

 60

			Hip adductors			Hip abductors
		Adductor	Adductor	Adductor	Gluteus	Gluteus	Gluteus
		longus	brevis	magnus	maximus	medius	minimus
	P1F	78.3	40.2	41.9	4.8	33.7	22.8
	P1M	81.1	35.8	38.8	5.5	26.8	16.0
	P2F	76.8	42.8	43.3	2.4	35.5	25.5
	P2M	80.1	38.1	40.3	4.4	30.5	19.6
	P3F	75.4	43.2	44.4	5.6	38.7	27.6
	P3M	79.9	38.8	40.7	4.5	33.5	21.9

Table 62 : Means and standard deviations of pedal force lateral deviation in XY plane from experiment, joint torque minimization based simulation and muscular activity minimization based simulation (respectively , and ) for a) maximum pedal force exertion experiment and b) pedal force perception experiment.

 62

	a)
	P1F	4.7 ± 1.6	-1.8 ± 1.2	-1.7 ± 1.9
	P1M	5.5 ± 1.7	-3.3 ± 2.0	-2.5 ± 2.0
	P2F	5.4 ± 2	-1.8 ± 1.4	-1.9 ± 1.9
	P2M	5.9 ± 1.9	-3.7 ± 2.2	-2.2 ± 2.0
	P3F	5 ± 2.6	-2.0 ± 1.7	-2.4 ± 1.8
	P3M	6.9 ± 2.2	-3.4 ± 2.5	-2.5 ± 2.2
	Short women	6.6 ± 1.5	-1.6 ± 1.5	-1.4 ± 2.2
	Average men	6 ± 1.7	-2.5 ± 1.7	-1.9 ± 1.9
	Tall men	4.4 ± 2.3	-3.6 ± 2.1	-3.1 ± 1.5
	All	5.6 ± 2.1 G***, P***	-2.7 ± 2.0 G***, P***	-2.2 ± 2.0 G***
	b)
	Very low	3.3 ± 3	1.9 ± 5.9	3.3 ± 6.8
	Low	4 ± 2.7	0.7 ± 5.7	0.8 ± 6.5
	Medium	4.4 ± 2.7	-1.1 ± 2.5	-0.4 ± 3.6
	High	5 ± 1.9	-1.9 ± 1.4	-1.6 ± 2.3
	Maximum	5.6 ± 3	-1.8 ± 1.2	-2.6 ± 1.8
	Short women	6 ± 2.3	0.8 ± 6.3	1.4 ± 7.3
	Average men	4.7 ± 2.1	-0.6 ± 1.9	-0.2 ± 2.8
	Tall men	3.6 ± 2.6	-1.7 ± 2.2	-2.0 ± 3.2
	All	4.7 ± 2.6 G***, F***	-0.7 ± 3.9 G***, F***	-0.5 ± 4.8 G***, F***
	*p<0.05, **p<0.01, *** p<0.001. G: group of subjects; P: Pedal position; F: force level

Comparison of simulated and experimental normal and transversal forces

Table 63 : Means and standard deviations of simulated (for muscular activity minimization and joint torques minimization) and experimental normal forces a) maximum pedal force exertion and b) pedal force perception experiments.

 63

			Normal force (N)
	a)	Muscular activity minimization	Joint torque minimization	Experiment
	P1F	-258 ± 117	-260 ± 119	-279 ± 150
	P1M	-170 ± 86	-166 ± 82	-218 ± 114
	P2F	-250 ± 122	-254 ± 123	-277 ± 155
	P2M	-149 ± 84	-141 ± 72	-185 ± 97
	P3F	-259 ± 135	-261 ± 133	-284 ± 159
	P3M	-145 ± 86	-136 ± 79	-176 ± 106
	Short women	-124 ± 89	-125 ± 87	-145 ± 103
	Average men	-225 ± 123	-232 ± 124	-257 ± 163
	Tall men	-247 ± 101	-236 ± 105	-287 ± 103
	All	-205 ± 117 G***, P***	-203 G***, P*** ± 117	-237 G***, P** ± 138
			Normal force (N)
	b)	Muscular activity minimization	Joint torque minimization	Experiment
	Very low	-44 ± 22	-38 ± 27	-44 ± 23
	Low	-60 ± 29	-57 ± 35	-63 ± 32
	Medium	-91 ± 50	-93 ± 58	-100 ± 55
	High	-152 ± 80	-156 ± 86	-167 ± 93
	Maximum	-250 ± 114	-266 ± 123	-303 ± 144
	Short women	-84 ± 82	-81 ± 86	-93 ± 85
	Average men	-165 ± 128	-170 ± 134	-176 ± 155
	Tall men	-161 ± 108	-173 ± 126	-203 ± 149
	All	-141 ± 114 G***, F***	-146 G***, F*** ± 125 -163 G***, F*** ± 143
	*p<0.05, **p<0.01, *** p<0.001. G: group of subjects; P: Pedal position; F: force level

Table 64 : Means and standard deviations of simulated (for muscular activity minimization and joint torques minimization) and experimental transversal forces a) maximum pedal force exertion and b) pedal force perception experiments.

 64

			Transversal force (N)
	a)	Muscular activity minimization	Joint torque minimization	Experiment
	P1F	18 ± 13	16 ± 23	-49 ±
	P1M	43 ± 29	34 ± 32	-71 ±
	P2F	18 ± 16	20 ± 22	-55 ±
	P2M	45 ± 30	28 ± 27	-70 ±
	P3F	18 ± 16	20 ± 18	-44 ±
	P3M	37 ± 27	27 ± 26	-71 ±
	Short women	13 ± 13	8 ± 16	-49 ±
	Average men	26 ± 19	20 ± 24	-72 ±
	Tall men	45 ± 28	40 ± 24	-58 ±
	All	30 ± 25 G***, P***	24 G*** ± 25	-60 G**, P** ± 37
			Transversal force (N)
	b)	Muscular activity minimization	Joint torque minimization	Experiment
	Very low	-1 ± 5	-3 ± 6	-4 ±
	Low	1 ± 5	1 ± 7	-8 ±
	Medium	6 ± 8	5 ± 12	-16 ±
	High	13 ± 10	13 ± 16	-32 ±
	Maximum	20 ± 17	30 ± 27	-62 ±
	Short women	5 ± 9	3 ± 11	-22 ±
	Average men	8 ± 12	8 ± 16	-36 ±
	Tall men	14 ± 17	23 ± 28	-33 ±
	All	10 ± 14 G***, F***	13 G***, F*** ± 22	-31 ± 32 G*, F***
	*p<0.05, **p<0.01, *** p<0.001. G: group of subjects; P: Pedal position; F: force level

PERCEPTION ET CONTROLE DE LA DIRECTION DE L'EFFORT SUR PEDALE 3

  MANNEQUINS NUMERIQUES ET EVALUATION ERGONOMIQUE DES COMMANDES AUTOMOBILES ............................................................................................................... 1.2 CONTEXTE DE L'ETUDE ET OBJECTIF DE LA THESE ..................................................... INTRODUCTION ................................................................................................. 2.2 METHODES ET PROCEDURES EXPERIMENTALES ........................................................ 2.3 ANALYSE DE LA GENE RESSENTIE ........................................................................... 2.4 ANALYSE DU MOUVEMENT DE DEBRAYAGE ............................................................. 2.5 ANALYSE DE L'EFFORT APPLIQUE SUR LA PEDALE ..................................................... 2.6 DEFINITION D'INDICATEURS D'INCONFORT POUR LE DEBRAYAGE ................................ .1 INTRODUCTION ................................................................................................. 3.2 METHODES ET PROCEDURES EXPERIMENTALES ........................................................ 3.3 OBSERVATIONS EXPERIMENTALES ......................................................................... 3.4 SIMULATION DE LA DIRECTION D'EFFORT ET DE L'AJUSTEMENT POSTURAL ...................

	1	Introduction
	Synthèse La technologie de la simulation numérique devient incontestablement un enjeu majeur pour
	1 l'industrie automobile, en particulier pour la conception des véhicules. L'utilisation de INTRODUCTION
	1.1 modèles numériques du corps humain devrait, à terme, faciliter considérablement le travail de
	conception et limiter le recours aux tests sur maquettes physiques grandeur nature. L'objectif
	2 de ces mannequins numériques est de prendre en compte des facteurs humains en amont d'un IDENTIFICATION DE CRITERES OBJECTIFS D'INCONFORT A PARTIR DU MOUVEMENT « MOINS
	CONTRAINT » 2.1 projet de conception de produit. Dans le cadre de la conception de commandes automobiles, il
	s'agit en particulier de considérer la dynamique du mouvement et la force exercée lors de
	l'utilisation pour prédire le mouvement et l'inconfort associé.
	L'objectif de cette partie est de souligner les limitations actuelles des mannequins numériques
	par rapport à la conception des commandes automobiles afin de définir les objectifs de ce
	3 travail de thèse. CAPACITE MAXIMUM, 5 CONCLUSION GENERALE commandes sont :
		Des critères objectifs d'inconfort pour identifier et corriger les sources d'inconfort
		Des efforts sur commande réalistes (magnitude et direction)
		Des postures d'utilisation réalistes

4 MODELE MUSCULO SQUELETTIQUE ET CONTROLE DE

LA DIRECTION D'EFFORT 4.1 INTRODUCTION ................................................................................................. 4.2 SIMULATION DE LA DIRECTION D'EFFORT ...............................................................

1.1 Mannequins numériques et évaluation ergonomique des commandes automobiles

Considérant les caractéristiques techniques d'une commande automobile et leurs effets sur l'utilisateur, les besoins des ingénieurs en conception pour améliorer l'ergonomie des

1.1.i Limitations des mannequins actuels utilisés dans l'automobile Actuellement

  , trois éditeurs se partagent le marché des mannequins numériques : Siemens avec Jack™, Dassault Systèmes avec Human Builder™ et Human Solutions avec Ramsis™. 'inconfort, qui doit être différencié du confort, est une notion subjective, fonction d'une multitude de facteurs. Les hypothèses les plus souvent émises sont qu'il résulte entre autres de facteurs biomécaniques tels que les interactions mécaniques avec l'environnement et les

	1.1.ii	Modélisation de l'inconfort
	contraintes mécaniques internes affectant le système musculo-squelettique. Les modèles
	d'inconfort doivent d'une part prédire l'inconfort ressenti lors d'une tâche mais aussi
	permettre l'identification des sources de la gêne en vue d'une correction. La principale
	difficulté réside donc dans l'identification de critères d'inconfort. Chaque tâche ayant ses
	propres contraintes, ces indicateurs sont par définition spécifiques à une tâche. Néanmoins,
	l'identification des critères peut reposer sur une approche générique pour pouvoir être étendu
	à un large panel de mouvement.
	Ces trois mannequins numériques intègrent des outils spécifiques à l'évaluation ergonomique
	d'un véhicule (enveloppe d'atteinte, analyse de champs de vision, …) mais aussi des
	méthodes de prédiction de posture de conduite. Cependant, les méthodes proposées ne
	prennent pas ou peu en compte l'effet de la production d'un effort sur la posture. Par ailleurs,
	l'identification d'indicateurs d'inconfort pertinents à partir de ces mannequins est
	relativement difficile pour les non-experts en ergonomie.

L1.1.

iii Relation entre l'effort et la posture

  Débuté à la fin du projet FAC et en parallèle du projet DHErgo, cette thèse a pour objectif de développer les modèles biomécaniques de l'homme pour l'évaluation ergonomique des commandes automobiles. L'objectif de cette thèse est double. Il s'agit d'une part de proposer une méthode générique d'identification de critères d'inconfort pertinents pour évaluer une tâche et d'autre part, de comprendre les mécanismes de contrôle de la force et de la posture durant une tâche en vue d'améliorer les méthodes de simulation de mouvement. Une approche combinant expérimentation et modélisation a été choisie La pédale d'embrayage et le mouvement de débrayage ont par ailleurs été retenu pour l'étude.

	2	Identification de critères objectifs d'inconfort à partir du mouvement
		« moins contraint »
	2.1	Introduction
	Du point de vue d'un ingénieur conception, le problème n'est pas de distinguer un produit
	bien conçu d'un produit mal conçu mais plutôt de pouvoir choisir le meilleur design pour un
	produit parmi un ensemble de solutions potentielles. La réalisation d'une tâche étant plus ou
	moins contrainte par l'environnement, on peut supposer que la gêne ressentie diminuerait
	dans le cas où l'utilisateur pourrait effectuer ses propres ajustements. Ces mouvements
	« moins contraint » pourraient alors être utilisés comme mouvement de référence pour évaluer
	de nouvelles solutions de design. Dans cette étude réalisée dans le cadre du projet européen
	DHErgo, le concept de mouvement « moins contraint » ou « neutre » (Dufour and Wang,
	2005) a été utilisé pour identifier des paramètres biomécaniques pertinents pour la définition
	d'indicateurs d'inconfort du mouvement de débrayage.
	1.2	Contexte de l'étude et objectif de la thèse
	L'étude présentée dans ce manuscrit hérite d'une longue collaboration entre Renault et
	l'IFSTTAR sur les problématiques d'évaluation ergonomique des véhicules, avec à la fin des
	années 80 avec le développement du mannequin numérique MAN3D et plus récemment, suite
	au projet européen REALMAN, celui d'un outil de simulation de mouvement appelé RPx.
	L'approche proposée pour la simulation de mouvement et la prédiction d'inconfort dans RPx
	étant principalement cinématique et donc pas adaptée à des tâches impliquant une production
	d'effort, un projet de collecte de données de capacités d'effort sur commandes automobile
	(projet FAC) a donc été lancé par Renault et l'IFSTTAR en 2008. La même année, les deux
	entités se sont par ailleurs engagées avec d'autres partenaires universitaires et industriels
	européens dans un projet de recherche soutenu par l'UE visant entre autres à développer des

La production d'un effort et l'ajustement postural sont intimement liés que ce soit par les caractéristiques d'une tâche (force à appliquer, direction de l'effort, position de l'effecteur, …) ou par les capacités physiques d'une personne considérée. La simulation de posture et d'effort réalistes sur une commande automobile passe donc par la collecte de données d'effort dans des conditions expérimentales proche de la réalité mais aussi par une meilleure compréhension des mécanismes de contrôle moteur en jeu lors de la réalisation d'une tâche. méthodes de simulation de mouvement et de prédiction de l'inconfort prenant en compte la dimension dynamique d'un mouvement ou d'une tâche.

  partir des dimensions anthropométriques collectées et du module Bodybuilder du logiciel Ramsis, un avatar de chaque participant a été créé. Ce modèle individualisé a été ensuite exporté dans le logiciel RPx pour une étape visant à définir la position des marqueurs placés sur le participant dans le repère du segment auquel ils appartiennent. Le principe consiste à superposer, dans un espace calibré, une représentation schématique du modèle sur différentes prises de vue à la posture du participant. La cinématique du mouvement a été reconstruite par cinématique inverse à partir d'une méthode d'optimisation qui minimise l'écart entre la position des marqueurs réels mesurés et des marqueurs virtuels liés au mannequin numérique. Puis, les couples articulaires ont été calculés par dynamique inverse à partir de la cinématique du mouvement, des efforts externes et des propriétés massiques et inertielles des segments corporels. Les efforts articulaires ont été calculés itérativement à chaque instant en isolant les segments corporels du plus distal au plus proximal.

	2.2.ii	Traitement des données

A

Deux instants-clés du mouvement de débrayage ont par ailleurs été définis pour chaque essai pour faciliter l'analyse. Le premier correspond au début de course, i.e. quand le pied gauche commence à appuyer sur la pédale et le second à la fin de course.

4.i Comparaison des mouvements de débrayage imposés et moins contraints

  Pour chaque question et variable, un test du X 2 a aussi été effectué pour tester l'hypothèse d'indépendance des distributions de lignes et de colonnes. L'analyse a porté en particulier sur la perception de la position de la pédale en début et fin de course ainsi que la perception de la résistance de la pédale.Six questions ont été posées pour évaluer les positions de début et de fin de la pédale en fonction de sa hauteur, sa distance et de sa position latérale. Pour chaque question, trois réponses possibles ont été proposées aux sujet: trop élevée/bon/trop basse pour hauteur de la pédale, trop loin/bon/trop près pour la distance, trop à gauche/bonne/trop à droite pour la position latérale. Le test du X 2 a montré des effets importants du type de configuration sur la position de la pédale en début et fin de course. En début de course, les configurations de pédales imposées ont été jugées trop élevées, trop près et trop sur la droite, alors que les configurations moins contraintes ont été évaluées comme bonne dans les trois directions pour plus de 90% des essais. Cependant, l'ajustement de la pédale n'a pas amélioré pour autant la perception de la position de fin de course. Au lieu de cela, les configurations de pédales moins contraintes ont été plus souvent perçues comme « trop basse » et « trop éloigné » que les configurations imposées. Par conséquent, les sujets ont eu tendance à améliorer le début de course au détriment de la fin.La répétabilité des sujets à noter l'inconfort a été évaluée en calculant l'écart maximum à la moyenne sur les trois répétitions de la configuration PCA2. Pour plus de la moitié des sujets, cet écart était supérieur à 5, signifiant un potentiel saut de catégorie d'inconfort entre deux répétitions.

	pédale a
	la chevi
	l'enfonc
	gauche
	Etant par définition indépendante, les effets des trois variables (groupe de sujets, la l'abduct
	configuration, le type de configuration) sur la note d'inconfort ont été analysées par analyse
	de variance. Les résultats montrent une influence statistiquement significative des trois
	variables (p<0.001). En particulier, les configurations moins contraintes ont en moyenne été
	évaluées comme moins inconfortable que celle imposées. En outre, la configuration a, elle
	aussi, une importante influence sur la note, BMW1 étant la configuration générant le moins de
	gêne et PCA1 et REN3 celles en causant le plus.
	2.4	Analyse du mouvement de débrayage
	L'évaluation de la résistance de la pédale a été faite en utilisant l'échelle CR10 de Borg avec
	5 niveaux de perception d'effort : très faible/faible/moyen/élevé/très élevé. La plupart des
	réponses ont qualifié la résistance comme étant moyenne (54%) et dans une moindre mesure
	comme faible (26%) ou élevé (18%). Seule la configuration a eu un effet sur la perception de
	la résistance de la pédale. BMW4 a été jugée comme ayant la pédale la moins dure alors que
	PCA1 et REN3 ont été évaluées comme étant les pédales les plus dures.
	L'influence du siège, la longueur de la course de la pédale ainsi que son inclinaison ont
	également été évalués par les sujets. Pour plus de 90% des réponses, le siège n'a eu aucun Au niveau de l'ajustement de leur position, les pédales ont en moyenne été déplacées de 8.8
	effet sur la perception de l'inconfort. La course de la pédale d'embrayage a été estimée soit mm vers l'avant, 21 mm vers la gauche et 16.8 mm vers le bas. Globalement, les sujets ont
	trop longue, soit à une bonne longueur à peu près 50-50. L'inclinaison de la course a été la tous eu tendance à ajuster la pédale dans la même direction, c'est-à-dire plus loin du siège,
	plupart du temps estimée (72%) comme bonne. plus bas et plus à gauche.
	2.3.ii Les angles articulaires de la jambe gauche ont été analysés au début et en fin de course Analyse des notes d'inconfort
	Lors de chaque essai, il a été demandé aux sujets d'évaluer la gêne ressentie lors du débrayage (Figure 72). Des tests t appariés ont été effectuées pour estimer s'il existait des différences
	à l'aide d'une échelle de notation de type CP50, 0 pour une gêne imperceptible à 50 pour une significatives entre les angles articulaires pour les pédales imposées et ceux pour les pédales
	gêne extrêmement élevée.

Les mouvements de débrayage enregistrés ont été analysés pour expliquer la gêne ressentie par les sujets. Tout d'abord, dans la mesure où l'ajustement de la pédale à entrainer une amélioration du début de course au détriment de la fin, les postures à ces deux instants-clés vont être analysées en comparant les mouvements moins contraints avec ceux imposés.

Ensuite, comme il est apparu que la perception de la résistance de la pédale dépendait principalement de la configuration, la force appliquée sur la pédale (amplitude et direction) ainsi que ses répercussions au niveau des couples articulaires vont être analysées. Ces analyses doivent permettre d'identifier les paramètres biomécaniques pertinents pour la définition d'indicateurs d'inconfort du mouvement de débrayage. 2.moins contraintes. Les tests ont montré des différences significatives en termes d'angles de flexion/extension pour les trois articulations du membre inférieur (hanche, genou et cheville) ainsi qu'en termes d'angle d'abduction/adduction de hanche. L'ajustement de la position de la

Table 65 : Matrice de corrélation avec les coefficients de Pearson Bravais.

 65 p<0.05, **p<0.01, *** p<0.001. Seuls les couples articulaires sont significativement corrélés avec notes d'inconfort, en particulier pour le genou. Le couple articulaire du genou est également significativement corrélé avec les composantes tangentielles et normales de force appliquée sur la pédale, ainsi qu'avec la direction d'effort. Ainsi, pour chaque configuration, on peut supposer qu'il existerait une direction optimale d'effort, contrôlée par la composante normale de l'effort, qui minimiserait le couple au niveau du genou et donc qui minimiserait la gêne ressentie. La sollicitation de la composante normale de l'effort (composante non motrice) peut aussi expliquer les effets du groupe de sujets sur les différentes composantes de l'effort et les couples articulaires, en particulier par le fait que cette composante était plus sollicitée par les sujets jeunes que par les sujets âgés.

		F Normale	F Tangent.		Couple Genou	Couple Cheville	Notes CP50
	F Normale	1	-0.1327*	0.8286***	-0.7848***	0.7137***	0.0723
	F Tangent.		1	0.093	-0.236***	-0.2493***	-0.0611
				1	-0.8364***	0.6541***	-0.0062
	Couple Genou				1	-0.5564***	-0.1489*
	Couple Cheville					1	0.1205*
	Notes CP50						1

*

Remerciements

Ramsis™ digital human model

2.7

Summary of processed data

FAC project

Number of participants 30

Anthropometry measurements 21

Clutch pedal trials 12 (maximum pedal force) + 7 (pedal force perception)

Excluded participant 2

Number of reconstructed trials for motion analysis 28 x 19 + 8 x 1 (3 rd maximum) -8= 532

The inverse kinematics problem can be formalized as:

7.8

Pelvis displacement for short women, average men and tall men according to the force level

a)

P1F

-4.3 ± 2.8 -5.8 ± 7.5 -1.7 ± 1.9 4.7 ± 1.6 P1M -6.7 ± 2.6 -10.8 ± 6.4 -2.5 ± 2.0 5.5 ± 1.7 P2F -4.5 ± 2.7 -5.9 ± 6.8 -1.9 ± 1.9 5.4 ± 2 P2M -7.2 ± 2.9 -11.1 ± 5.4 -2.2 ± 2.0 5.9 ± 1.9 P3F -4.4 ± 3.2 -5.9 ± 6.4 -2.4 ± 1.8 5 ± 2.6 P3M -7.3 ± 2.3 -10.8 ± 5.3 -2.5 ± 2.2 6.9 ± 2.2

Short women -3.3 ± 2.7 -5.9 ± 7.2 -1.4 ± 2.2 6.6 ± 1.5

Average men -6.3 ± 2.9 -7.4 ± 5.9 -1.9 ± 1.9 6 ± 1.7

Tall men -7.1 ± 2.2 -11 ± 6.1 -3.1 ± 1.5 4.4 ± 2.3 All -5.8 ± 3.1 G***, P*** -8.4 ± 6.7 G***, P*** -2.2 ± 2.0 G*** 5.6 ± 2.1 G***, P***

b)

Very low -9.6 ± 11.9 -15.1 ± 8 3.3 ± 6.8 3.3 ± 3 Low -8.4 ± 8.8 -12.2 ± 6.4 0.8 ± 6.5 4 ± 2.7 Medium -6.2 ± 6.1 -9.5 ± 6.9 -0.4 ± 3.6 4.4 ± 2.7 High -5.3 ± 3.7 -6.9 ± 5.5 -1.6 ± 2.3 5 ± 1.9 Maximum -3.5 ± 2.3 -6.7 ± 5.7 -2.6 ± 1.8 5.6 ± 3 Short women -2.0 ± 4.6 -7.1 ± 6.6 1.4 ± 7.3 6 ± 2.3

Average men -7.7 ± 7.5 -8.5 ± 7.1 -0.2 ± 2.8 4.7 ± 2.1

Tall men -7.7 ± 7.2 -12.1 ± 6.7 -2.0 ± 3.2 3.6 ± 2.6 All -6.1 ± 7.1 G***, P*** -9.5 ± 7.1 G***, F*** -0.5 ± 4.8 G***, F*** 4.7 ± 2.6 G***, F*** *p<0.05, **p<0.01, *** p<0.001. G: group of subjects; P: Pedal position; F: force level 6 Appendix Average men -7.4 ± 5.9 -6.2 ± 4.1 -6.3 ± 2.9

Comparison of simulated and experimental pedal force directions

Tall men -11 ± 6.1 -7.8 ± 3.6 -7.1 ± 2.2 All -8.4 ± 6.7 G***, P*** -6.0 ± 3.9 G***, P*** -5.8 ± 3.1 G***, P*** b)

Very low -15.1 ± 8 -15.6 ± 9.7 -9.6 ± 11.9 Low -12.2 ± 6.4 -11.3 ± 7.1 -8.4 ± 8.8 Medium -9.5 ± 6.9 -7.1 ± 5.6 -6.2 ± 6.1 High -6.9 ± 5.5 -5.2 ± 3.8 -5.3 ± 3.7 Maximum -6.7 ± 5.7 -2.8 ± 2.5 -3.5 ± 2.3 Short women -7.1 ± 6.6 -5.8 ± 7.7 -2.0 ± 4.6

Average men -8.5 ± 7.1 -7.8 ± 7.2 -7.7 ± 7.5

Tall men -12.1 ± 6.