Mathieu ; Delangle
email: mathieu.delangle@irccyn.ec-nantes.fr

Jean-François François Petiot

Emilie Poirson

ASSESSING THE DIFFERENCES BETWEEN NUMERICAL METHODS, CAD EVALUATIONS AND REAL EXPERIMENTS FOR THE ASSESSEMENT OF REACH ENVELOPES OF THE HUMAN BODY

Keywords: Reach assessment, Experimental trials, Computer aided design (CAD), Human behaviour in design

come

Assessing the differences between numerical methods, CAD evaluations and real experiments for the assessement of reach envelopes of the human body

INTRODUCTION

The modelling of human reach envelope is widely used by designers to design and assess the accessibility of environments (for example to improve the physical design of manufacturing workplaces [START_REF] Chaffin | Improving digital human modelling forproactive ergonomics in design[END_REF] or assess the reachability of an automated teller machine for persons in a wheelchair [START_REF] Marshall | Using hadrian for eliciting virtual user feedback in Design for all[END_REF]). Many tools and practices are based on anthropometrics data to perform these ergonomics evaluations [START_REF] Roebuck | Anthropometric methods: Designing to fit the human body[END_REF]. Existing database (e.g. ANSUR [START_REF] Gordon | anthropometric survey of u.s. army personnel: Methods and summary statistics[END_REF], NHANES (NHANES, 1996) are generally chosen as the reference population. The input data are rarely measured from the real population considered for a study. To assess the accessibility of users, numerical methods, based on simple body dimensions extracted from the database, are commonly used and adapted to the environment (use case). Although numerical methods are faster and less expensive than the involvement of sample-user to test prototypes, the only use of these anthropometric data, involves a significant bias in the results of the ergonomic study [START_REF] Moroney | Empirical reduction in potential user population as the result of imposed multivariate anthropometric limits[END_REF]. For example, the users of a common product, independently of the age or gender, cannot always be physically compared to a military [START_REF] Garneau | Including preference in anthropometry-driven models for design[END_REF]. Moreover, while these data might be sufficient for univariate cases, particular care must be taken for multivariate problems, that can introduces specific complexities [START_REF] Garneau | A comparison of methodologies for designing for human variability[END_REF]. Three dimensional graphical representations of anthropometric data, called digital human model, are also used to analyse and design the intended product or environment. During the past decade, software models of the human body allow an estimate of the reach envelope or the vision cone of a parameterized user in a virtual environment [START_REF] Chaffin | Digital human modeling for vehicle and workplace design[END_REF]. These analyses are usually based on the manikin's inverse kinematics (IK) capability to evaluate the manikin's arm reachability in the 3D space. Human modeling tools have the ability to simulate the reach envelope of a virtual human, based on his body part dimensions and joints limits. Based on the kinematics and posture of a human, Digital Human Modelling (DHM) software are usually used instead of numerical and statistical reach models. One of the major reasons for their use seems to be that increasing the visual representation of the design problem would increase the reliability of the assessment. However, although these technologies allow a representation of the design problem in 3D environment, the ergonomic assessments are still mainly based on structural body dimensions [START_REF] Brolin | Consideration of anthropometric diversity -methods for virtual product and production development[END_REF]. Numerous DHM software include models based on static biomechanical proprieties of human body, trying to take into account factors that might affect the reach capacity as the mobility of shoulder and the spine [START_REF] Chaffin | On simulating human reach motions for ergonomics analyses[END_REF]. Unfortunately, these IK methods may not always be very biomechanically sophisticated and behavioural representative [START_REF] Chaffin | Simulating reach motions[END_REF]. The different existing methods of reach evaluation have to allow designers to perform accessibility assessments as close as possible to the reality, without building prototypes and using participants to make experimentations. Nevertheless, it is important to know the discrepancies compared to reality that can occur using these methods. In this study, the maximum standing reach envelopes from experiment with participants were recorded and used to assess the validity of 1) the static numerical model based on the external anthropometry, and 2) the kinematically generated reach assessments of a human model, given by the CAD modeling tool CATIA. The results obtained using a numerical method, a computer aided-design evaluation and experimental assessments are compared, and the discrepancies are discussed.

COMPARATIVE STUDY

The present study consists in comparing the evaluation of accessibility of sample-users, considering three different ways (Figure 1). The first way is to register a real experiment with a population of users. These results are used as references for the comparison. The second way consists in using the anthropometric measurements of this same population, declined with different tools:  Numerical approach: the data are used in input of a numerical model, simulating the accessibility;  Computer aided design approach: digital human ergonomic tools are used to predict the population's reach.

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Figure 1. Synopsis of the study

Application case

The task proposed is a 2D reaching task of the hand of the participant in the frontal plane (Figure 2), thereof being standing in front of a wall. The aim is to compare the results found experimentally with those not involving to call a user sample given by models (given by methods commonly used in human-centred design), and thus to verify the accuracy of the different reach assessment methods.

EXPERIMENT

Sampling

Forty participants (twenty five males and fifteen females), adult volunteers, were sampled in the study, covering a wide spectrum of physical characteristics, from 1482 mm for the smallest stature, to 1930 mm for the highest. The average (SD) of stature of the subjects were 1735 (95.4) mm. No one of these volunteers reported motor disabilities or particular physiological limitations. The sample is considered as representative of the general population.

It was a deliberate decision to not skew the data by "excluding" persons in the panel (e.g. old or disabled), in order to not bias the comparison. Indeed, the accessibility study of persons with specific physical limitations will depend of more parameters that would make the comparison more difficult.

The test

The test is a standard accessibility situation. We seek to define the accessibility area of each user, which may correspond to the distance of reachability of products on a shelf for example. To give a target to the users, we designed a task with electric switches. A total of 84 switches could be reached on a plate, constituting 168 measurement points (Figure 2) -one high and one down point for each switch.

Participants were asked to touch the switches by colour strips (with their left or right arm according to the side), given 12 black, 32 white, 44 green, 46 blue and 34 red reachable points. The device was designed to be adjusted at the participant's shoulder height for a wide range of human physical characteristics (designed from 2.5th and 97.5th percentile for women and man stature of ANSUR database), allowing to perform the tests for a large user population. Because most anthropometric data presented in databases represent nude body measurements and to permit reliable comparison with database approach, experiments were performed with light clothing (nude dimension and light clothing being regarded as synonymous for practical purposes).

ICED15 The plate was positioned on a wall with the help of two adjustable vertical axes for precise positioning of the bottom plate at shoulder height (Figure 2 Right). Reach measurements were made relatively to a body reference point (shoulder joint) and to a measurement apparatus point (bottom of the plate). The subject was positioned in the centre of the plate with the feet fixed regarding the floor. In order to check if the subjects does not lift their feet to increase their vertical reach, an electrical position sensor was positioned under their heels indicating if the feet were off the ground or not. When the visual signal was triggered, the test was stopped and the subject repositioned. This situation has to represent the functional reach, that is to say the maximal distance one can reach forward beyond arm's length while maintaining a fixed base of support in the standing position [START_REF] Duncan | Functional reach: Predictive validity in a sample of elderly male veterans[END_REF], [START_REF] Duncan | Functional reach: A new clinical measure of balance[END_REF].

For each individual, the reached switches were identified and noted in a table in order to draw the reach envelope.

EVALUATION METHODS

Creation of the common anthropometric database

When using methods for ergonomic evaluations, anthropometric modelling can be either directly observed from anthropometric characteristics of the current users, or statistically derived from characteristics for the intended target population. In order to limit the statistical biases in the comparison, the presented evaluations were all based on the anthropometric characteristics of the subjects who performed the experimental tests. External anthropometry being the type most frequently available and collected, it was decided to collect some "direct"' measurements of external link-length anthropometry. Data in Table 1 were recorded to predict the upper body accessibility for each participant, and were collected in a laboratory environment from the 40 individuals. It was expected that this number would provide a manageable database for the development and validation of the comparative study.

Static numerical evaluation

The aim is to evaluate the reach characteristics from the recorded external anthropometrics that might be found in anthropometric database (Table 1). This methodology is based on the design limits approach, which is a common method used in design problems, where data about human physical characteristics are directly applied to solve design problem. So, the maximum reach envelope is modelled (Figure 3) by an arc circle, with a radius equal to the arm length of the operator and the shoulder as point of rotation [START_REF] Farley | Some principles of methods and motion study as used in development work[END_REF], (NASA-STD-3001, 1995). All points within this envelope (shaded area) were considered as reach by the subject. Knowing the switches coordinates on the plate and the anthropometrics characteristics of individuals, a program was implemented allowing to automatically determinate which switches the participants theoretically reached. The results are presented in section 5.

Computer aided evaluation

The use of computer aided ergonomics systems during product development is a well-established methodology of which there are numerous examples [START_REF] Marshall | Using hadrian for eliciting virtual user feedback in Design for all[END_REF], [START_REF] Case | Virtual fitting trials in 'design for all[END_REF], [START_REF] Das | Technical note. computer-aided human modelling programs forworkstation design[END_REF]. Many software, all providing different manikins model and ergonomics tools, are available to the designer [START_REF] Delangle | Helping the designer to choose his software: Comparative analysis of human modeling tools[END_REF], [START_REF] Ranger | Evaluation de l'offre anthropom'etrique pour la conception de produits[END_REF]. A Human simulation technology commonly provided is the assessment of the reach envelope of a digital human model (DHM) within a digital mock-up (DMU) [START_REF] Chaffin | On simulating human reach motions for ergonomics analyses[END_REF]. In numerous situations, and particularly for multidimensional design problems, these simulations, based on the kinematics and posture of a human figure model are used instead of statistical reach models. Nowadays, a lot of fields of engineering, as in automotive [START_REF] Hanson | Digital Human Modeling in Automotive Product Application[END_REF], [START_REF] Abdel-Malek | Human reach envelope and zone differentiation for ergonomic design[END_REF] or in the inclusive design area [START_REF] Marshall | Hadrian: a virtual approach to design for all[END_REF], [START_REF] Porter | Beyond jack and jill: designing for individuals using hadrian[END_REF], use these technologies to predict the reachability of an environment. This section offers to assess the validity of the kinematics reach envelope generated with a digital human model. CatiaV5 and its Human modeling module Delmia were used to assess the reachability of the environment. This software uses the manikin's inverse kinematics capability to evaluate the manikin's arm reachability in the 3D space (joints ranges of motion of the manikin). The maximal reach envelope, including spine bending, is created to represent the maximum reach limits. As for the previous method, the evaluation was performed based on the experimental recorded data (Table 1).

The virtual manikin was parameterized on the model of each participant. The model of user trials that was performed with real people was taken and moved into the virtual world. The position of the avatars and the dimensions of the problem have been specified by the designer to be completely representative of the real situation. A display of the reach assessment is depicted in Figure 4. Results are discussed section 5.

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Figure 4. CAD evaluation of the maximal reach envelope of a participant.

RESULTS / DISCUSSION

Global comparison

Results from experiments and evaluations were collected for each of the forty participants. To assess the global reach, the number of switches reached by each participant was recorded. For each method, the mean and the associated standard deviation were calculated for each evaluation (Table 2). Intrinsically to the experiment, the overall reach envelope is highly correlated with the stature of the subject. The higher the stature, the higher the number of reached switches. In order to highlight the differences between the evaluations (without the stature influence), results were normalized by the stature of the participant. Resulting means and standard deviations obtained are depicted in Figure 5. This graph allows the comparison of the global results of each evaluation.

The static numerical model appears to be the method that underestimates the most the results, with an average of 36 reached points. Then, the assessment performed with the computer aided ergonomics tool provides more accurate results compared to the experiment, with an average of 45 switches.

Comparing with that obtained experimentally (55 switches), the static numerical and CAD evaluations represent respectively 64% and 82% of the total reach really obtained. The lowest standard deviation (Table 2) corresponding to the static numerical evaluation, represents the disparity of reach only related to differences in anthropometric characteristics.

In spite of the simple nature of the presented task (two-dimensional), the results show important differences between the two evaluations. The reach capacity is not only correlated with the static dimensions but depends on other external variables. This comparison being done for the overall reach, it is involved that a subject could reach more switches as expected (regarding his physical characteristics) on certain parts of the envelope, and less than expected in others. That's why, it addition to the global results, the reach was also studied in a local way.

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Local comparison

The CAD assessment provides results the closest to the experiment, with a total error of 672 against 1330 for the numerical evaluation (Table 3). The total errors are defined as the differences between the total numbers of points reach using each evaluation methods and the experiment, representing the deviations from the "reference" result (considered arbitrarily as the results of the experiment).

To study the localizations and frequencies of these errors, a local comparison was conducted. The normalized theoretical reached envelope obtained from static numerical evaluation was used as a basis for comparisons to highlight the behaviour of the reach depending on the hand position during the movement. To avoid the anthropometric differences, each envelope was normalized between 0 (corresponding to the initial shoulder position) and 1 (dimensional extremity of the hand). The reach errors were determined for each participant and aggregated for each method. For example, shown in Figure 6 is the aggregate error for each zone of the envelope, obtained from the CAD evaluation. The grid corresponds to the discretization of the domain, representing the different points (switches) reachable on the plate. A positive error (on the vertical axis) corresponds to unexpected reached points (outside the theoretical arc circle), and negative error to unreached points that were expected to be reached (into the theoretical arc circle).

In order to make the comparison between the evaluations, the reaches were drawn on a two dimensional normalized graph (Figure 7). The reach envelopes defined from the static numerical evaluation are represented by an arc circle, with 0 as centre and 1 as radius. The shaded area represents the theoretical reach envelope based on the numerical model. Any point include into this arc circle is theoretically reached. Conversely, any point outside this area is theoretically considered as unreached, considering the body structural dimensions. The lines represent the mean profile of the reaches for the forty subjects, i.e. (50% included and 50% excluded). Thus, all the parts of a line not included in this area describe the set of points reached out of the theoretical reach.

The maximum vertical reach (corresponding to points above the head) is higher than expected for the experimental evaluation. The main reason of this increase is probably due to the complex model of displacement of the shoulder, and particularly of its flexion, allowing the subjects to increase their reach upwardly, beyond their external structural dimensions. With the foot fixed on the ground, the strategy to enhance the vertical reach is to increase the flexion of the shoulder and to change the alignment of the shoulder and the pelvis. The same observation can be made for the CAD evaluation, where the maximal extended reach is located beyond the head. This might be explained by the 5

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degrees of freedom shoulder model of the CATIA mannequin, allowing elevation / depression movement, i.e. an upwardly augmentation of the reach [START_REF] Ranger | Evaluation de l'offre anthropom'etrique pour la conception de produits[END_REF], (Dassault-Systemes, 2000).

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According to Figure 7, the reach envelopes obtained experimentally appear to be overall more important than those predicted by the models. Nevertheless, the greatest difference is localized on the lateral region of the envelope. The body being not constrained (excepted the feet), the maximal reach strongly depends on the lateral reach capacity, represented by the medio-lateral balance stability and the pelvis rotation ability of the subject [START_REF] Brauer | Lateral reach: a clinical measure of medio-lateral postural stability[END_REF]. It appears that it is quite difficult to take into account this kind of behaviour using only a computer-aided design environment. Most users of digital human models will attempt to overcome these limitations by kinematic manipulations of the human model. However, due to a lack of quantitatively valid models of postures and motions for extreme reaches, the results may not be useful. For example, a user of a digital human model is unlikely to guess the correct amount of lateral pelvis rotation when simulating lateral reach aspects (Abdel-Malek and Arora, 2009), [START_REF] Reed | A new approach to modeling driver reach[END_REF].

The CAD assessment, including biomechanical considerations intrinsic to the human model of the software, made more realistic results compared to a model only based on the static anthropometric dimensions. Nevertheless, this model seems to still underestimate the range of extended reach involved by the human behaviour. This is consistent with the general problems associated with Human-CAD models of reach, which might have difficulty to predict reaches in the outer borders of the reach envelope [START_REF] Kozey | Considerations related to modeling the maximum reach envelope (mre) as a sphere[END_REF].

CONCLUSION

This study compared results of a reach assessment obtained from an experiment, a numerical model and a computer aided evaluation. In spite of the simple nature of the presented design problem (twodimensional), the results show important differences between the three evaluations. Those obtained using the CAD evaluation were the most accurate, compared to those really obtained from the experiment. However, the local comparison showed that the reach envelopes provided by the CAD assessment were not always consistent with those obtained experimentally, depending on the location of the hand. The results indicate that the CAD model used to obtain maximum reaches gave predictions that are likely to be substantially in error. The majority of the discrepancy between the observed maximum reach capability and that predicted by the model is located on the lateral reach area, and is particularly associated with the medio-lateral balance stability and the pelvis rotation ability of the subject. This means that a strictly kinematical approach to predicting maximum reach capability is not likely to be accurate. Anthropometric design problems associated with human physical characteristics depend on many other factors. Several task considerations should be taken into account in order to construct a reach envelope, as the nature and requirements of the task to be performed, the body position while reaching, the whole body movement capabilities and restraints. Thus, human models included in the DHM'S cannot include all the subtleties of human movement observed in the large variety of coping strategies that may be used to achieve success. This is particularly true for more complex evaluations, where the actual posture adopted may be important, such as restricted access tasks. Human models used for reach assessment should consider all these human capacities. A solution could be to numerically include biomechanical and behavioral data in the inverse kinematics model, in order to take into account the extended reaches. Unfortunately, this will be difficult to apply in the case of complex multivariate design problem, due to the wide variety of human capacities and reach strategies involved. That is why design evaluation should be made in order to naturally take into account these strategies. Thus, virtual reality tools, allowing a designer to be physically involved in the simulation, seem to be a promising approach in the field of reach evaluation in design. Indeed, because these new technologies allow the reproduction and simulation of real movement of a human interacting with an environment, virtual simulations might take into account more factors influencing the reach that existing method. Development of virtual reality technologies could be a key approach to improve the user-centred design methodology, especially for complex multivariate problems, to develop a powerful ergonomic design tool, including biomechanical, structural, behavioural and cognitive models.

Figure 2 .

 2 Figure 2. Left: plate composed of 84 switches (168 points) used in the experimental trials. Right: reaching test (the subject has to reach switches on the right (left) of the plate with his right (left) arm).

Figure 3 .

 3 Figure 3. Static model of reach envelope (shaded area) of the arm calculated from external anthropometric dimensions of the subject.

Figure 5 .

 5 Figure 5. Mean and standard deviation of the number of reached switches divided by the stature for the 40-member sample, obtained from the four methods

Figure 6 .

 6 Figure 6. Plot of the overall model error in the various regions of the reach envelope, obtained from the CAD evaluation (with the numerical model as reference). The frame represents the initial right shoulder position in the normalized domain.

Figure 7 .

 7 Figure 7. Normalized reach lines (50% accommodation) obtained from the experiment (orange) and from the CAD evaluation (blue). The shaded area represents the normalized reached area obtained from the static numerical model. The frame represents the initial right shoulder position in the normalized domain.

Table 1 .

 1 Anthropometric characteristics of the subjects (in mm) participating to the experiment, with the average and the standard deviation (excerpt).

		1	2	…	40	Mean	S.D.
	Gender	M	F	…	M	-	-
	Shoulder height	1485	1450	…	1420	1455	87.7
	Shoulder width	470	430	…	460	456	39.8
	Arm length	750	730	…	710	719	41.5
	Stature	1735	1705	…	1715	1735	95.4

Table 2 .

 2 Numbers of reached switches for each participant, measured from the experiment and predicted through numerical and CAD method, with means and standard deviations (excerpt).

		1	2	…	40	Mean	S.D.
	Experiment	107	102	…	52	96	25
	Numerical model	76	57	…	30	63	22
	CAD evaluation	87	67	…	44	79	26

Table 3 .

 3 Total number of reached switches for each participant (excerpt) and the reach error compared to the experiment results.

		1	2	…	40	Total	Error
	Experiment	107	102	…	52	3846	0
	Numerical model	76	57	…	30	2516	1330
	CAD evaluation	87	67	…	44	3174	672


Emilie Poirson

LUNAM, IRCCYN Matthieu Delangle

Comparative analysis of human modeling tools

Digital Human Modeling tools simulate a task performed by a human in a virtual environment and provide useful indicators for ergonomic, universal design and representation of product in situation. The latest developments in this field are in terms of appearance, behaviour and movement. With the considerable increase of power computers,some of these programs incorporate a number of key details that make the result closer and closer to a real situation. With the differences in terms of performance, qualities, limitations, the choice of the tool becomes complicated in this wide range of possibilities. In this context, we propose to study and compare the most human modelling software available on the market, and thus provide an aided decision tool to help the designer to get the most adaptable tool.

Introduction

In the recent decades, emerged commercial software based on numerical models of man: the virtual human [START_REF] Badler | Virtual humans for animation, ergonomics and simulation[END_REF]. The Digital Human Modeling Software (DHMS) have been introduced in industry firstly to facilitate a faster design process [START_REF] Mathiassen | Tools for integrating engineering and ergonomics assessment of time aspects in industrial production[END_REF]. With the increasing of computer power, the use of DHM software became unavoidable in the life cycle of products, where the design has to answer to end-user expectations, including their need for usability [START_REF] Mahdjoub | A collaborative Design for Usability approach supported by Virtual Reality and a Multi-Agent System embedded in a PLM environment[END_REF]. With an iterative process of product evaluation, the correction and adjustments are quicker [START_REF] Chaffin | Digital human modeling for vehicle and workplace design[END_REF]. As in all categories of software package, the quality and accuracy increase continuously, to meet the demand of industrials and researchers ( [START_REF] Chaffin | Improving digital human modelling for proactive ergonomics in design[END_REF], [START_REF] Landau | Ergonomics software tools in product and workplace design -a review of recent developments in human modeling and other design aids[END_REF]). The proliferation of tools becomes problematic for the designer who has sometimes a multitude of functions that are not suitable for his application case.

The first step of our study consisted in listing all the comparable software and to select the comparison criteria. Then a list of indicators is proposed, in three major categories: degree of realism, functions and environment. Based on software use, literature searches [START_REF] Almeida Moura | Analise comparativa de ferramentas computationais de modelagem e simulaçao humana para aplicaçao em projetos de situaçoes produtivas[END_REF] and technical reports ( [START_REF] Beagley | Human body modeling as a human factors engineering tool[END_REF], [9], [START_REF] Seidl | Ramsis: a new cad-tool for ergonomics analysis of vehicles developed for the german automotive industry. automotive concurrent/ simultaneous engineering[END_REF], for example), the table of indicator is filled and coded from text to a quinary format, in order to performed comparative analysis. The last part presents the results and the outlooks of the study.

DHM tools comparison : methodology

An exhaustive list of 32 commercially available 3D modeling software, computer programs used for developing a mathematical representation of any three-dimensional surface of objects was determined (step 1, Figure 1). A part of these tools defined as generic modelers (ie software allowing purely artistic entities modeling without real anthropometric approach) have been removed and a list of reachable human modelers was obtained (step 2, Figure 1). For example, Rhinoceros is a NURBSbased 3D modeling software, commonly used for industrial design, architecture, marine, jewelry design but not manikin design. The step 3 (Figure 1) is the collection and selection of the differentiating criteria to evaluate the software.

Comparison table 3.1 Criteria

A list of indicators is defined to perform an objective comparison between all software (Table 1). To generate this list, websites and forums about DHM tools are analyzed as technical manuals of Santos [START_REF] Abdel-Malek | Development of the virtual-human santos[END_REF], Ramsis [START_REF] Van Der Meulen | Ramsisthe leading cad tool for ergonomic analysis of vehicles[END_REF], Jack [START_REF] Ugs | Jack User Manual -Version 5[END_REF], 3DSSPP [START_REF]3D Static Strength Prediction ProgramTM Version 6.0.5 User's Manual, Mars[END_REF] for example. All the menus given a choice of functions are explored. The criteria are classified in 3 main classes:

• Class 1 : Degree of realism. This class is used to compare the reliability of the representation of the model and its movements or respect of human physical constraints, for example.

• Class 2 : Functions. This class is very important for ergonomic and fatigue studies. It is associated with existing functions in the software to perform some analysis on the virtual model (Reach envelop or Fatigue model for example).

• Class 3 : Environment. Includes criteria for the creation and manipulation of the environment available in software. If the criteria are mostly obvious, definition is to be precise for others. Physical limits stands for taking into account the physical constraints of articulations as knee and elbow. The Gender graduation stands for the the evolution of body forms, more or less pronounced. Complements are all the personalization of the manikin with clothes or accessories, and their movement during an animation (Dynamic of complements).

Few simplifications on the criteria were done. The number of degrees of freedom, joints and segments seemed confusing for a non expert-user. They have been gathered under the label Accuracy joint chain. Secondly, the difference between motion and animation is low and not always understood; the criteria were aggregated. Finally, in the data of environment, only the first, very important for ergonomic and the last one (essential to reach all trades and new applications) were kept. With the different transfer format, even if the software doesn't allow to create an environment, the manikin can be included in an existing one in another tool. It seems to us not primordial for this first study. The list of criteria has now 25 items.

3.2 Filling method (step 4, Figure 1)

A table containing software and the 25 criteria is built. Based on software use, literature searches, manual study and by contacting users of different softwares, each cell of this table is filled with textual data. This step, long and fastidious was led with rigor and completeness. The different scales were not pre-defined, ignoring a priori which information will be collected.

Coding of criteria (step 5, Figure 1)

To perform a comparative analysis, it is essential to formalize textual data contained in the table. Criteria (Table 1) were split in 3 categories. The first one is the binary criteria, answering yes or no for the presence of the function (b). The second class (q1) contains those evaluated on a 5 points scale, quantifying the criteria (0-criterion not satisfied, 1-criterion partially satisfied, 2-criterion moderately satisfied, 3-criterion rather well satisfied, 4-criterion completely satisfied). The last category is also a quinary scale about the precision of data (q2): for example, the skin representation can be inexistant (0), existing but not very modifiable (1) to fully configurable (5).

Compairison of tools

After coding data from text to a coded format for the entire comparison table, multivariate statistical analysis (Principal Components Analysis and Hierarchical Ascendant Classification) are used to perform a decision tree (Figure 5).

Principal Component Analysis

The Principal Component Analysis (PCA) is used to reduce the dimensions of the space allowing a representation of the proximity between individuals and variables and to find the underlying dimensions. The matrix was analyzed using standardized PCA. The two first factors represent 64,04% of variability. In our case (Figure 5 ), the first Principal Component is mainly composed of criteria based on the realism of the manikin, including its movements. Software are clearly in 2 groups on this axis: a first on the right side of the graph, composed of Poser/Daz/MakeHuman/Quidam, software allowing DHM simulation with an high quality graphics rendering. The left group has a littler graphical definition but with an higher number of analysis functionality. The second Principal Component is correlated to criteria based on analytic tools as collision detection or fatigue model. This confirms the intuitive classification of criteria performed.

On this first plan, Santos seems to be isolated, due to the fact that 15% of information stay on the 3rd principal 3 component not represented here. To precise this plan, we use the Hierarchical Ascendant Classification method.

Hierarchical Ascendant Classification

In order to provide a partition of the software and to define groups, similar from an analytic point of view, a hierarchical ascendant classification (HAC) [START_REF] Hair | Multivariate Data Analysis[END_REF] has been done. The principle of HAC is to build a hierarchical tree (dendrogram, Figure 6), which shows the level of each aggregation according to the dissimilarity between the products. The parameters of the method are the definition of the distance for computing the dissimilarities and the linkage rule, computed through the Ward criteria. The dotted line represents the truncation and visualizes that three homogeneous groups were identified :

• C1 composed of Santos, Jack and Catia/Delmia, • C2 composed of Anybody, 3DSSPP, Ramsis, Human-Cad and Sammie,

• C3 composed of MakeHuman, Quidam, Poser and Daz.

The classification perform by the HAC appears to be consistent with the geometrical representation of the proximity between individuals implement by the PCA. Firstly, the four software on the right of the Figure 5 are grouped together in C3, divided in the two same subgroups Daz/Poser and Quidam/Makehuman resulting the proximity of these software. Secondly, the same couples Humancad/Sammie and Jack/Catia are found. In Figure 5, Ramsis and Anybody seem to be related (very close to each other), but not in the hierarchical tree. The cosine matrix of observations shows that the tools are strongly linked with the third component F3. That's why L2 and L7 are not directly interconnected on the hierarchical tree. [START_REF] Chaffin | Digital human modeling for vehicle and workplace design[END_REF] 4 The same event arrive between Santos and Delmia/Jack. The most relevant representation seems to be or a 3D mapping from the PCA or, the dendrogram of the CAH. We will use the last cited, representation of the Hierarchical Ascendant Classification.

From the dendrogram, a protocol of choice of the best suited software to the expected use can be defined. The first step of the dissimilarity is performed between the class C3 and C1/C2 (Figure 6). The comparison of C3 link to C1/C2 on our criteria show a superiority of the performance of analysis (Table 1, column 2). The first question for the user will be about the analysis need. Then, the classes are divided in sub-classes. For example, in the class C3, two sub-classes are distinguished : the second group is autonomous to perform animations and motion capture (mocap), instead of the first group which have to export their digital human to another software allowing to perform animation and interaction with the avatars in mocap.

From the hierarchical tree and by identifying what are the main discriminating criteria, it is possible to define a protocol to determine from minimum questions, what is the best suited software to the expected use. Some criteria (variables) identified through the PCA and HAC are grouped together in the form of questions to guide quickly the search towards a specific group of software. Other criteria are then explicitly evaluated allowing accurate selection of the software.

Five questions (regarding the "capacity to perform analysis", "realism of mannequin", "Animation of mannequin", "dynamic of analysis" and "human appearance of mannequin"), involving some discriminants criteria, allow to quickly select corresponding software. These questions are coded in a friendly interface following the algorithm given below. where stands Perform analysis for "Do you need functions to perform analysis on the virtual mannequin (eg static analysis, field of vision, collision detection, reach envelop...) ?", Realistic for "Do you need a realistic virtual mannequin with an high graphical rendering ?", Animation for "Do you need to perform animation and mocap inside my software ?" Dynamic analysis to "Do you need to do dynamic analysis Human appearance seems obvious.

Algorithm of selection

These simple questions can be answered by the designer to guide him in his choice of software.

Conclusion and perspectives

This paper presented a comparison of digital human modeling software allowing to perform a decision making tools to help the designer to choose his software. Twelve digital human modeling software have been presented and compared. From a table including characteristics of software through al list of 25 comparison criteria, Principal Components Analysis and Hierarchical Ascendant Classification were used to build a decision tree. The procedure to select the most adaptable is finally exposed. The next step of the project is to guide the selection by visual perception, not only questions. In the case of recommender for Design Human Modeling software, we want also to improve the acquisition of anthropometric data, extracted from pictures for example. The designer may present situations that he would like to represent, and after interpretation of images variations(postures, ergonomics, anthropometry ...) software would be advisable with a manikin to customize.

The human modeling is essential in the lifecycle of the product, allowing a very good communication between all the actors of the life of product. Integration of an adapted DHM tools in the product life cycle allows to perform both a more efficient design and more sustainable products. The aim of the presented procedure is the conception of a tool allowing to the designer to quickly determine what are the types of solutions that best suit his needs. In our study, the tools are dedicated to helping the designer to find the most suitable software. However, the methodology can be adapted to all kinds of applications, for example in the design of products. Indeed, software of our study may be replaced by a sample of a product randomly generated (Monte Carlo's method...) and also evaluated using criteria (height, width, color, texture, materials...). Thus, using our method, the discriminating criteria may be identified and automatically encoded in the decision making tools allowing to offer to the designer a sample of shapes adapted to their needs, by answering some questions.

Acknowledgements

Thanks go to B.Novarini, for his contribution on software study.

  for our study are (Figures2-4) Jack (Siemens), Ramsis (Human Solutions), HumanCad (Nexgen Ergonomics), 3DSSPP (University of Michigan), Poser (Smith Micro), MakeHuman (freeware), Anybody (Anybody Technology), Catia (Dassault Systemes), Daz Studio (DAZ 3D Inc), Quidam (N-Sided), Santos (University of Iowa), Sammie (Sammie CAD Ltd).

Figure 1 :

 1 Figure 1: SYNOPTIC OF THE METHODOLOGY OF EVALUATION OF PRODUCTS

Figure 2 :

 2 Figure 2: MANIKIN OF JACK (a), RAMSIS (b), HU-MANCAD (c) AND 3DSSPP (d).

Figure 3 :

 3 Figure 3: MANIKIN OF POSER (a), MAKEHUMAN (b), ANYBODY (c) AND DELMIA (d)

Figure 4 :

 4 Figure 4: MANIKIN OF DAZ (a), QUIDAM (b), SAN-TOS (c) AND SAMMIE (d)

Figure 5 :

 5 Figure 5: INDIVIDUALS REPRESENTATION INTO A TWO-DIMENSIONAL PLANE.

Figure 6 :

 6 Figure 6: DENDROGRAM OF THE HIERARCHICAL ASCENDANT CLASSIFICATION

Table 1 :

 1 CRITERIA OF THE 3 CLASSES

if

  Perform analysis ="no" then if Animation="no" then soft = MakeHuman or Quidam else soft = Poser or Daz Studio end if else if Animation="yes" then if Realistic="yes" then

	soft = Santos
	else soft = Jack or Catia
	end if
	else if Human appearance="yes" then
	soft = Anybody modeler
	else if Dynamic analysis="yes" then
	soft = Ramsis or 3DSSP
	else soft = Sammie or HumanCad
	end if
	end if
	end if
	end if


M Delangle

E Poirson

J F Petiot

Study of human accessibility: physical tests versus numerical simulation

Keywords: Population model, Numerical simulation, Comparative analysis

Introduction

In the field of universal design, taking into account the physical characteristics of the target population during the design process is essential. However, human variability causes difficulty to meet the requirements of comfort and security of a certain percentage of the user population. That's why human factors engineering allows to optimize the interactions with the user systems, considering the human body variability of population. Physical ergonomics allows to study interactions among humans and other elements of a system, to create an environment that is well-suited to a user's physical requirements. Many tools and practices are in common use today that assist in basic assessments of accommodation, and indicate to the engineer how to design for the variability in body dimensions (or anthropometry), capability and age, in the target user population [Roebuck, 1995].Commonly used approaches are the "population models" (where a group of individuals representing the target-user population interacts with a real prototype) and the use of anthropometric data where body dimensions are used to fit the relevant product dimensions.

To avoid building expensive physical mock-up, the designer can use anthropometrics database (e.g. ANSUR [START_REF] Gordon | anthropometric survey of u.s. army personnel: Methods and summary statistics[END_REF], NHANES [for Disease Control and Prevention, 1994]) generally used to configure sizes of a product, to improve perception (e.g., comfort, safety, etc.) across a population to maximize the satisfaction of the greatest number of users. Thus, several methods based on these anthropometric data [START_REF] Committee | Guidelines for Using Anthropometric Data in Product Design[END_REF] are used to design adapted products and assess the degree of accommodation. Existing database may be chosen as the reference population and use directly ("boundary mannequins"...) or through regression techniques ("population model", hybrid approaches"...) [START_REF] Garneau | Including preference in anthropometry-driven models for design[END_REF]]- [Holler, 2010]- [START_REF] Reed | Anthropometric and postural variability: limitations of the boundary manikin approach[END_REF]. For example, "2D boundary mannequin" approach can used database, where relevant dimensions are taken directly from an anthropometric database to create manikins at the xth percentiles of the measure of interest, to represent a specified accommodation [START_REF] Garneau | A comparison of methodologies for designing for human variability[END_REF]. Defining the mannequins so that they correctly represent the future users is frequently simplified into the use of a standard setup based on a few percentile values, or a combination of predefined key anthropometric variables [START_REF] Hanson | Digital Human Modeling in Automotive Product Application[END_REF]. In reality, a xth percentile may show poor correlation between body size and may not include physiological situations occurring frequently as tall people with relatively short arms or small people with relatively long legs [START_REF] Marshall | A taskbased design for all support tool[END_REF]. Although numerical methods are faster and less expensive than building prototypes, the only use of these anthropometric databases [START_REF] Moroney | Empirical reduction in potential user population as the result of imposed multivariate anthropometric limits[END_REF], often old, are not always representative of the target user population. In fact, they often consist of specifics surveys (military...), and typically provide only very limited information concerning children and people who are older and disabled [START_REF] Marshall | A taskbased design for all support tool[END_REF]. Moreover, this methods are generally used in univariate case study (to determine the appropriate allocation of adjustability to achieve a desired accommodation level), where most problems are multidimensional. That is why, although the use of digital data allows to avoid to perform experimental tests and building prototypes, methods based on this principle still pose questions about the ease of use and reliability compared to reality. So, we propose to perform an accessibility study compare results obtained from simple numerical models (using raw anthropometric data) with those obtained experimentally.

Comparative study

In this study, four "simply" tests are perfomed to evaluate accessibility based on physical capacity (not including preference).

Methodology

This paper considers two ways to assess accessibility in which experimentation and database approach are applied in accessibility evaluation (Fig. 1). We focus on the accessibility of the upper body. A population of various people is selected. They are asked to participate to two different tests of accessibility (reached envelope): discret test A and continuous test B. In the same time, some of there physical characteristics are measured to create an anthropometric database. These database aims to used as input of the two numerical simulations to determine the theoretical reached envelope. The two experiments proposed, each time realized physically and numerically, aims to highlight the differences of experimental results in comparison of real accessibility, and so verify if the numerical simulation of the upper body reach envelope might be considered as realistic.

Subjects and anthropometric measurement

The experiments were conducted with 40 adult volunteers, all of being French students or teachers of the Ecole Centrale de Nantes. In order to create a database of measurements, four anthropometric characteristics were measured for each of them (Fig. 2), namely the stature, the shoulder height, the shoulder width and the arm length (Table .1). Twenty five males and fifteen females were sampled in the study, covering a wide spectrum of physical characteristics, from 1482 mm for the smallest stature, to 1930 mm for the highest.

The A Test

Experiment A allows to simulate realistic arm accessibility situations through interaction with physical points (switches).

Description and physical realization Principle: Accessibility to switches located in front of an individual and positioned on a vertical plate (effect of the body position while reaching).

A plate is positioned on a vertical wall with the aid of two adjustable vertical axes for precise positioning of the bottom plate at shoulder height He (Fig. 3). Reach measurements are made relatively to a body reference point (shoulder joint) and to a measurement apparatus point (bottom of the plate). The subject is positioned in the center of the plate with the feet fixed regarding the floor (position sensor is positioned under the heels of the subject indicating if the feet are off the ground or not). For each individual, the reached switches are identified and noted in a table in order to draw the reach envelop. A total of 84 switches can be reached on the plate, which constituted 168 measurement points (two positions for each switch). With a view to ease of notation and understanding, participants are asked to touch the switches by color strips (with his left or right arm according the side), giving 12 black , 32 white, 44 green, 46 blue and 34 red reachable points. The device is designed to fit at shoulder height for a wide range of human physical characteristics (designed from 2.5th and 97.5th percentile for women and man stature of ANSUR database), allowing to perform the tests for a large user population. Because most anthropometric data presented in databases represent nude body measurements and to permit reliable comparison with database approach, experiments are performed with light clothing (nude dimension and light clothing being regarded as synonymous for practical purposes). For both tests A1 et A2, the task demands can be defined as "reach switches with at least one finger touches the switch and pushing it".

Test A1 : The subject keeps the feet fixed to the floor and the body must stay fixed (spinal column axis) relative to the vertical axis of the center of the plate, and reaches switches one arm at a time. Test A2 : The subject keeps the feet fixed to the floor but is allowed to twist (changing the position of the upper body) to reach the switches, one arm at a time.

Numerical simulations (Tests A)

Reached switches calculation : The objective is to compute the reached switches according to the anthropometric characteristics recorded during experimentations (experimental database). The reach envelop is defined by a circle arc, with the arm length Larm as radius and the position of the shoulder Oh as rotation point (Fig. 4). So, knowing the coordinates of the switches on the plate, the theoretically reach points computed from arm length and shoulder width can be defined. Reach envelope is defined by assuming that the arm makes a perfectly circular arc with the shoulder as point of rotation. Moreover, as the database usually provide data for only one arm, we use the value of the right arm and admit that both are perfectly symmetrical. Knowing the switches coordinates on the plate and the anthropometrics characteristics of individuals, a programme is implemented (using Matlab R2012b) allowing to automatically determinate which switches are theoretically reached by the arms. The realisation of experimental tests A1, A2 and numerical calculations provides, for each participant: _ Switches with the fixed body _ Switches achieved with the upper body flexible _ Switches that should have been achieved according to the model used

Experimental tests B 2.4.1 Description and physical realization

Objective : Marking of the boundaries of arm reach of an individual in standing posture. The participant stays in front of an erasable marking device, positioned on a vertical wall on which he must draw his reached envelope (Fig. 5). Then, a photo of the drawing is taken and treated thanks to a Matlab program to accurately determine the area of the envelope (Fig. 6). As tests A, reach envelope of tests B are constructed considering one-handed operation with fixed foot, and movements or not of the torso. Note : reach design dimensions or envelopes for design use should be constructed considering grasp requirements which may affect the functional reach envelope. Fingertip touch resulting in the largest reach dimensions appropriate for touch controls, envelopes are marked to define the task as "finger touch" function (one finger touches an object without holding it) in order to avoid much as possible grasp effect (reducing the reach envelope).

Test B1 : The subject keeps the feet fixed to the floor and the body fixed (spinal column axis) relative to the vertical axis of the center of the plate, and marks his reach envelop with both arms simultaneously. Test B2 : The subject keeps the feet fixed to the floor but is allowed to twist (changing the position of the upper body) to marks his reach envelop one arm at a time.

Numerical simulations (Tests B)

The objective is to determine the reach envelope area of a subject based on his anthropometric characteristics, such as might be found in a database. That is, reach envelope area for each participant (defined by envelopes Fig. 7), is calculate using the arm length [iB] and the shoulder width [ih] (all other quantities being calculate from these values). Reach envelopes should be constructed considering the shoulder (arm rotation point) as reference point in the envelope calculation to determine the different arc circle areas. So, the total area reached on the wall by the arms of the individual is numerically simulated by Eq.1, where both arm lengths are considered as the same. Area A4 is a common part for both arm; so to avoid redundancy in the calculations, this area is not taken into account in the definition of the total envelope.

Results and discussion

Each participant was asked to perform the four experiments. Results are collected, providing for each of them the experimental results obtained with fixed body, with flexible upper body and from calculations, in the case of discret tests (Table 2) and continuous tests (Table 3). Figure 9 and 8 represent the results of tests A1, A2, B1 and B2 measured from experiments, and the numerical calculations plotted against stature for the 31-member sample, with regression line.

Table 2. Numbers of reached switches for each participants, measured from experiments A1, A2 and numerically calculated. ΔA1 and ΔA2 are respectively the differences of reached switches between the calculations and experiments.

Table 3. Reach areas for each participants, measured from experiments B1, B2 and numerically calculated. ΔB1 and ΔB2 are respectively the differences of areas between the calculations and experiments.

x y

The aim of the experiments was to prove :

-the influence of the stretching of the body on the reach -the quality and the limits of the numerical simulation thanks to the real experiment.

Regarding table 2, we notice firstly that the number of switches activated might be very different: from 30 to 105. It is very dependent of the stature: Pearson coefficient is 0.83 against 0.78 with the length of arms. The average standard deviations of the distributions A, A1 and A2 are respectively equal to 21, 23 and 24. We compare the significance of the stretching of the body. Results for A2 are obviously superior to A1 but the range is hight: an average increasing of 40% of switches. The second point is about the validation of our model. If we compare A1 to A from simulation, we find an average difference of 8 switches. Knowing that the switches are organized clothes one from the other, this difference is acceptable. But in the case of A2, this same average is 33. As it might be expected, the model used is not adapted to body with a possible stretching. On the Fig. 7, it can be seen a vertical translation between the linear regressions obtained from experimental data A1, A2 and calculated data A.

The calculate data B and experimental data obtained through tests B1 and B2, plotted with respect to stature, are shown Fig. 8. The average standard deviations of the distributions B, B1 and B2 are respectively equal to 0:12, 0:15 and 20:2. This dispersion of the calculated data might be explained by the differences of anthropometry of subjects, accentuated by the fact that the male and female data are processed simultaneously, explaining the cloud of points obtained. For example, for a same height, the arm lengths can vary greatly from one individual to another, directly affecting the reach envelope, which is calculated from the value of arm length and shoulder width. The results obtained experimentally by tests B1 are quite close to those obtained theoretically, with a mean difference ΔB1 = (B calculated -B1 experimental ) = 0,031. So, the reach areas measured from experiment B1 might be considered as acceptable in comparison of reality. However, as for the test A2, values for B2 are widely superior in comparison to those calculated with a mean difference of ΔB2 =(B calculated -B2 experimental )= -0,239.

In general, it is observed that the values obtained experimentally are higher compared with numerical calculations from anthropometric data of individuals passing the tests. The results show the wide range of results which these various methods all of which are in common use can provide to this extremely simple accessibility assessment. The tests A1 and B1 have allowed to show that in a fixed position, the results obtained by numerical method are quite close to reality. However, results from tests A2 and B2 have shown numerical calculations varying greatly from reality. This is due to the fact that in this situation, accessibility is measured when the body is in motion and engaged in a physical activity. Indeed, the numerical model (which uses raw numerical data) being based on static anthropometry, does not take into account the functional anthropometry induced by dynamic behaviour of the human body (stretch of muscle, body motion ...) intrinsic to real situations.. These finding are consistent with other studies ( [START_REF] Kroemer | Engineering physiology: Bases of human factors/ergonomics[END_REF]) involving more complex problem showing that reach limits are clearly dependent on the task, motion, and function to be accomplished by the reach action.

So, our study has demonstrated that (1) Stretching the body is far from negligible be in computing accessibility, and (2) the static model is not representative of the stretched body (functional anthropometry) but simply the right body (static anthropometry). It is also interesting to highlight the impact of the functionality of the task at hand. Indeed, testsA involving physical touch points, experiment shows that in this case, even if experiments are implemented to minimize this behavior, participants tend to unconsciously want to move his upper body to reach switches. The differences in behavior highlights the impact of real-life situations (notions of objective and performance) through the physical points (switches) on the results. So, this shows that task demands (touching the switches) might affect reach characteristics and measures. Initial study trials have shown the limits of the numerical simulation (based on raw anthropometric data) thanks to the real experiment (influence of the stretching of the body on the reach). The aim for further work is to use a kinematic model to predict dynamic reach, and compare results with those in this study. Moreover, study will be performed with more subjects in order to have a significant number of participants to create a new statistical model of accessibility according to the anthropometry. An other perspective would be to explore models of body fatigue regarding the required task. In other words, although stretching the muscles can increase the achievement envelope, if a such movement is harmful to humans, these kind of "extreme" skills have no interests in point of view of ergonomic.

Figure 1 .

 1 Figure 1. Synopsis of the study.

Figure 3 .

 3 Figure 3. Discret experimental test A1 (the subject Figure 4. Definition of accessibility of is switches reaching switches on the right of the plate from anthropometric data. with his right arm).

Figure 5 .

 5 Figure 5. Continuous experimental test B1 (left) with the feet and the body fixed, and test B2 (right) with the feet fixed and the upper body in movement.

Figure 6 :

 6 Figure 6: Image processing using matlab. Bright space represents the reach envelope area drawn by the participant. The dark area (rectangle) represents the paper sheet used as repository to calculate areas from pixels to square meters.

Figure 7 .

 7 Figure 7. Calculation of reach envelope areas.

Figure 8 .

 8 Figure 8. Results for A1 and A2 measured from experiments. and from numerical calculations, with regression line.

Figure 9 .

 9 Figure 9. Results for B1 and B2 measured from experiments and from numerical calculations, with regression line.

Table 1 . Anthropometric characteristics measured for each participants, with values in mm. Figure 2. Anthropometrics characteristics measured.

 1