F Moissenet

L Modenese

R Dumas

Alterations of musculoskeletal models for a more accurate estimation of lower limb joint contact forces during normal gait: a systematic review

Keywords: survey, joint reaction force, skeletal and joint model, muscle model, optimisation problem, validation

Musculoskeletal modelling is a methodology used to investigate joint contact forces during a movement. High accuracy in the estimation of the hip or knee joint contact forces can be obtained with subject-specific models. However, construction of subject-specific models remains time consuming and expensive. The purpose of this systematic review of the literature was to identify what alterations can be made on generic (i.e. literature-based, without any subject-specific measurement other than body size and weight) musculoskeletal models to obtain a better estimation of the joint contact forces. The impact of these alterations on the accuracy of the estimated joint contact forces were appraised.

The systematic search yielded to 141 articles and 24 papers were included in the review.

Different strategies of alterations were found: skeletal and joint model (e.g. number of degrees of freedom, knee alignment), muscle model (e.g. Hill-type muscle parameters, level of muscular redundancy), and optimisation problem (e.g. objective function, design variables, constraints). All these alterations had an impact on joint contact force accuracy but it was not possible to highlight any trend defining which alteration had the largest impact.

Introduction

Musculoskeletal modelling is a computational methodology used to investigate musculotendon forces and joint contact forces during a movement, which cannot be easily measured [START_REF] Chèze | State of the art and current limits of musculoskeletal models for clinical applications[END_REF][START_REF] Erdemir | Model-based estimation of muscle forces exerted during movements[END_REF][START_REF] Pandy | Muscle and joint function in human locomotion[END_REF]. In particular, recent literature has demonstrated that a high accuracy in the estimation of the hip or tibiofemoral joint contact forces can be obtained, but induced the use of an extensive personalisation of the model parameters [START_REF] Ding | In Vivo Knee Contact Force Prediction Using Patient-Specific Musculoskeletal Geometry in a Segment-Based Computational Model[END_REF][START_REF] Gerus | Subject-specific knee joint geometry improves predictions of medial tibiofemoral contact forces[END_REF][START_REF] Jung | Intra-articular Knee Contact Force Estimation during Walking using Force-reaction Elements and Subject-specific Joint Model[END_REF][START_REF] Kia | Evaluation of a Musculoskeletal Model with Prosthetic Knee through Six Experimental Gait Trials[END_REF][START_REF] Marra | A Subject-Specific Musculoskeletal Modeling Framework to Predict in Vivo Mechanics of Total Knee Arthroplasty[END_REF]. However, the construction of subject-specific models remains a time consuming procedure that requires not always available data, such as medical imaging.

Conversely, several components of generic musculoskeletal models may be altered that have noticeable influence on the estimated joint contact forces. Generic models, in this context, refer to original models established only from literature data and not subject-specific measurements. The present review focused on possible alterations of generic musculoskeletal models by assuming that, through a more accurate estimation of joint contact forces, they could reduce the need of model personalisation, and thus facilitate a broader use of musculoskeletal modelling.

Several data repositories have been made available and allow to evaluate model accuracy during different motion tasks. Bergmann et al. [START_REF] Bergmann | Hip contact forces and gait patterns from routine activities[END_REF] have disseminated in vivo measurements of hip contact forces, obtained through the use of instrumented hip implants, as well as kinematics and ground reaction forces and moments, during several daily activities (e.g. walking at different speeds, up and down stairs, sitting down, standing).

Similarly, Fregly et al. [START_REF] Fregly | Grand challenge competition to predict in vivo knee loads[END_REF] have disseminated a dataset concerning tibiofemoral medial and lateral contact forces during several variant of walking (e.g. normal, bouncy, medial trust, turn, acceleration). This data repository was completed with medical imaging data allowing the personalisation of a musculoskeletal model. These repositories have already been used in several studies and allowed to evaluate the accuracy of models with different levels of personalisation.

In this study, a systematic literature review was performed in order to identify what are the alterations that can be made on a generic musculoskeletal model to obtain a more accurate estimation of joint contact forces. The impact of these alterations on the accuracy of the estimated forces was appraised. More specifically, this study focused on lower limb musculoskeletal models and on hip and tibiofemoral joint contact forces during normal gait, because these forces have been now extensively validated against the previously mentioned in vivo measurements from instrumented implants.

Material & Methods

Search strategy

An electronic search was performed in Medline, Scopus, and Academic Search Premier databases. The logical (nested) expressions for the search were: musc* and contact and ((hip or knee or tibio*femoral) and (force* or load or reaction)) and (((force-measuring or instrumented or force-instrumented) and (replacement or implant or prosthesis)) or (valid* or accura* or in vivo measurement*)) not cadaver*. The search was based on the title, keywords and abstract. References cited by the articles remaining after applying the exclusion criteria (see below) were also cross-referenced as well as the articles citing them.

Exclusion criteria

The articles retrieved from the search strategy were reviewed according to the following exclusion criteria. Studies were omitted if they 1) were published only as conference proceedings, 2) were written in a language other than English, 3) did not refer to a generic musculoskeletal model, 4) did not alter a generic musculoskeletal model, 5) did not include validation against instrumented prosthesis measurements, and 6) did not focus on normal gait.

Note that the term generic musculoskeletal model refers in this systematic review to a model established only from literature data. It is the baseline situation in all included studies of this systematic review. However, body size and weight may have been personalised for scaling purposes. Moreover, depending on the study, the related generic musculoskeletal model can be different in terms of modelling approach and level of details.

Quality assessment

A customised checklist was developed on the basis of previous reviews in the field of biomechanics addressing connected topics [START_REF] Kainz | Estimation of the hip joint centre in human motion analysis: A systematic review[END_REF][START_REF] Peters | Quantification of soft tissue artifact in lower limb human motion analysis: A systematic review[END_REF] to assess the methodological quality of the selected studies. Each question was rated two (satisfying description or justification), one (limited details) or zero (no information). The 16-item quality checklist used in this review was: Q1: Are the research objectives clearly stated? Q2:

Is the study design clearly described? Q3: Is the scientific context clearly explained? Q4: Is the musculoskeletal model adequately described? Q5: Were the model alterations clearly described? Q6: Is the model for joint contact force estimation adequately described? Q7:

Were participant characteristics adequately described? Q8: Were movement tasks, equipment design, and set up clearly defined? Q9: Were the evaluation strategy appropriately justified? Q10: Were the analytical methods clearly described? Q11: Were the statistical methods justified and appropriately described (other than descriptive statistics)? Q12: Were the direct results easily interpretable? Q13: Were the main outcomes clearly stated and supported by the results? Q14: Were the limitations of the study clearly described? Q15: Were key findings supported by other literature? Q16: Were conclusions drawn from the study clearly stated?

Each study was evaluated independently by the three authors (FM, LM, and RD) for this assessment. In case of discrepancy, the original article was checked to ensure the correct coding, and a consensus was found between authors.

Data extraction

A customised data extraction form was developed to extract key details from each selected study. One author (FM) performed the data extraction and the two other authors (LM and RD) checked the final form to ensure reliability. The retained information consisted in: 1) the investigated joint (i.e. hip joint or tibiofemoral joint), 2) the definition of the generic musculoskeletal model (i.e. software used in the simulations, original model, number of degrees of freedom (DoFs) and of muscular lines of action, muscle paths (i.e. straight lines, via points, wrapping surfaces), method used to solve the muscle redundancy problem and joint contact model), 3) the validation dataset used to assess model accuracy, 4) the alterations applied to the model, and 5) their impact on the joint contact force accuracy. These themes were chosen to provide an overview of the methods of each selected study together with the presented alterations and associated results. When the methods were only partially described in the original article, comprehensive information was retrieved from references and author's previous works to provide comparable data across the selected studies.

Results

Search strategy yield

The search strategy allowed to identify 99 articles in Medline, 65 in Scopus, and 77 in Academic Search Premier, yielding to 141 articles without any duplicate (Fig. 1). According to the exclusion criteria, 20 articles were retained. Most of the excluded articles did not include validation against in vivo measurements from instrumented prosthesis. Others presented either a fully subject-specific musculoskeletal model [START_REF] Ding | In Vivo Knee Contact Force Prediction Using Patient-Specific Musculoskeletal Geometry in a Segment-Based Computational Model[END_REF][START_REF] Gerus | Subject-specific knee joint geometry improves predictions of medial tibiofemoral contact forces[END_REF][START_REF] Jung | Intra-articular Knee Contact Force Estimation during Walking using Force-reaction Elements and Subject-specific Joint Model[END_REF][START_REF] Kia | Evaluation of a Musculoskeletal Model with Prosthetic Knee through Six Experimental Gait Trials[END_REF] or a generic musculoskeletal model without alteration [START_REF] Heller | Influence of femoral anteversion on proximal femoral loading: measurement and simulation in four patients[END_REF][START_REF] Kim | Evaluation of predicted knee-joint muscle forces during gait using an instrumented knee implant[END_REF][START_REF] Modenese | Prediction of hip contact forces and muscle activations during walking at different speeds[END_REF][START_REF] Purevsuren | Prediction of medial and lateral contact force of the knee joint during normal and turning gait after total knee replacement[END_REF][START_REF] Stansfield | Direct comparison of calculated hip joint contact forces with those measured using instrumented implants. An evaluation of a three-dimensional mathematical model of the lower limb[END_REF][START_REF] Trepczynski | Patellofemoral joint contact forces during activities with high knee flexion[END_REF]. Four more articles were obtained by cross-referencing [START_REF] Dumas | Influence of joint models on lower-limb musculo-tendon forces and three-dimensional joint reaction forces during gait[END_REF][START_REF] Lund | Scaling of musculoskeletal models from static and dynamic trials[END_REF][START_REF] Manal | An electromyogram-driven musculoskeletal model of the knee to predict in vivo joint contact forces during normal and novel gait patterns[END_REF][START_REF] Steele | Compressive tibiofemoral force during crouch gait[END_REF], yielding the final set of 24 articles. Note that this final set was only composed of rigid body musculoskeletal models (some of them including deformable joints), while both rigid body and finite element musculoskeletal models were initially included.

Quality assessment and data extraction results are reported below. Details can be found in Table S1 (available as supplementary material) and Table 1, respectively.

Quality assessment

The overall score of each article was thus calculated by the sum of rated questions divided by the sum of applicable questions. The selected studies were all of high quality, with a scoring ranged between 80% and 100%, and a mean score of 92% (Table S1, available as supplementary material). However, several questions were only partially answered. For example, validation (or comparison with validation dataset) was not always explicitly mentioned as an objective (or a method), elements of the methods (i.e. the musculoskeletal model, computation method for joint contact forces, or the validation dataset) were described just through a reference to previous work, or limitations of the study were not clearly stated or omitted. Only 3 of the 24 selected studies (see Table 1) reported a statistical method other than descriptive statistics (i.e. mean, standard deviation, root mean square error, correlation).

Generic musculoskeletal models

More than half of the studies (13 out of 24) were based on specialised biomechanical software such as Opensim [START_REF] Delp | OpenSim: Open-source software to create and analyze dynamic simulations of movement[END_REF] and Anybody [START_REF] Damsgaard | Analysis of musculoskeletal systems in the AnyBody Modeling System[END_REF]. Six studies used Matlab (The Mathworks, USA) to perform the computations. Finally, SIMM (Motion Analysis Corporation, Musculographics, USA), ADAMS (MSC Software, USA) or custommade software were used in the remaining studies.

More than half of the selected studies (13 out of 24) used models based on the generic musculoskeletal model of [START_REF] Delp | An interactive graphics-based model of the lower extremity to study orthopeadic surgery procedures[END_REF]. The original or a variant of the model developed by Klein Horsman et al. [START_REF] Klein Horsman | Morphological muscle and joint parameters for musculoskeletal modelling of the lower extremity[END_REF] or by [START_REF] Arnold | A model of the lower limb for analysis of human movement[END_REF] were also used. Two further models were also developed by other authors [START_REF] Heller | Determination of muscle loading at the hip joint for use in pre-clinical testing[END_REF](Heller et al., , 2001;;[START_REF] Lin | Simultaneous prediction of muscle and contact forces in the knee during gait[END_REF].

The number of DoFs was related to the generic musculoskeletal model used in each study.

Briefly, the primary differences were observed at the hip, tibiofemoral, and patellofemoral joints were the number of DoFs were 3 or 6, 1 or 6, and 0, 1 or 6, respectively.

The number lines of action were also related to the generic musculoskeletal model used in each study and ranged between 11 [START_REF] Lin | Simultaneous prediction of muscle and contact forces in the knee during gait[END_REF] and 163 [START_REF] Chen | Prediction of in vivo joint mechanics of an artificial knee implant using rigid multi-body dynamics with elastic contacts[END_REF][START_REF] Modenese | Application of a falsification strategy to a musculoskeletal model of the lower limb and accuracy of the predicted hip contact force vector[END_REF][START_REF] Modenese | An open source lower limb model: Hip joint validation[END_REF][START_REF] Moissenet | Influence of the level of muscular redundancy on the validity of a musculoskeletal model[END_REF][START_REF] Zhang | Prediction of hip joint load and translation using musculoskeletal modelling with force-dependent kinematics and experimental validation[END_REF]. In all the selected studies, the muscle paths were enhanced by via points only (15) or with (9) the use of wrapping surfaces.

A majority of studies (17 out of 24) solved the muscle redundancy problem (also called the force distribution problem [START_REF] Crowninshield | A physiologically based criterion of muscle force prediction in locomotion[END_REF]) through an inverse dynamicsbased optimisation, while some others used a forward dynamics-based optimisation (Guess et al., 2014), an EMG-driven method [START_REF] Manal | An electromyogram-driven musculoskeletal model of the knee to predict in vivo joint contact forces during normal and novel gait patterns[END_REF], a reduction method [START_REF] Lundberg | Fine tuning total knee replacement contact force prediction algorithms using blinded model validation[END_REF][START_REF] Lundberg | Direct comparison of measured and calculated total knee replacement force envelopes during walking in the presence of normal and abnormal gait patterns[END_REF], computed muscle control method [START_REF] Hast | Dual-joint modeling for estimation of total knee replacement contact forces during locomotion[END_REF][START_REF] Thelen | Co-Simulation of Neuromuscular Dynamics and Knee Mechanics During Human Walking[END_REF], or a mixed approach [START_REF] Walter | Muscle Synergies May Improve Optimization Prediction of Knee Contact Forces During Walking[END_REF]. Proposed objective functions included the sum of muscle activations, or musculo-tendon forces, at different power, both weighted and not. Some studies included additional terms to the objective function, such as reserve actuators [START_REF] Modenese | Application of a falsification strategy to a musculoskeletal model of the lower limb and accuracy of the predicted hip contact force vector[END_REF][START_REF] Serrancoli | Neuromusculoskeletal Model Calibration Significantly Affects Predicted Knee Contact Forces for Walking[END_REF] or joint loads [START_REF] Demers | Changes in Tibiofemoral Forces due to Variations in Muscle Activity during Walking[END_REF][START_REF] Lin | Simultaneous prediction of muscle and contact forces in the knee during gait[END_REF][START_REF] Moissenet | Influence of the level of muscular redundancy on the validity of a musculoskeletal model[END_REF][START_REF] Moissenet | A 3D lower limb musculoskeletal model for simultaneous estimation of musculo-tendon, joint contact, ligament and bone forces during gait[END_REF]. In one study, a min-max method was applied on musculo-tendon forces [START_REF] Chen | Prediction of in vivo joint mechanics of an artificial knee implant using rigid multi-body dynamics with elastic contacts[END_REF]. Other objective functions [START_REF] Manal | An electromyogram-driven musculoskeletal model of the knee to predict in vivo joint contact forces during normal and novel gait patterns[END_REF][START_REF] Walter | Muscle Synergies May Improve Optimization Prediction of Knee Contact Forces During Walking[END_REF] were mainly based on the tracking of a set of variables (e.g. joint moments).

The computation of joint contact forces was mainly based on 1-point or 2-point rigid contact models (respectively 10 and 5 out of 24 studies). However, several studies introduced deformable structures by using a force dependent kinematics method [START_REF] Chen | Prediction of in vivo joint mechanics of an artificial knee implant using rigid multi-body dynamics with elastic contacts[END_REF][START_REF] Zhang | Prediction of hip joint load and translation using musculoskeletal modelling with force-dependent kinematics and experimental validation[END_REF], a deformable contact with viscous damping [START_REF] Guess | Concurrent Prediction of Muscle and Tibiofemoral Contact Forces During Treadmill Gait[END_REF], a rigid body spring model [START_REF] Hast | Dual-joint modeling for estimation of total knee replacement contact forces during locomotion[END_REF], or a surrogate contact modelling [START_REF] Lin | Simultaneous prediction of muscle and contact forces in the knee during gait[END_REF]. Some other studies are based on regression equations converting the varus-valgus moment to medial and lateral tibiofemoral contact forces [START_REF] Lundberg | Fine tuning total knee replacement contact force prediction algorithms using blinded model validation[END_REF][START_REF] Lundberg | Direct comparison of measured and calculated total knee replacement force envelopes during walking in the presence of normal and abnormal gait patterns[END_REF], or on prosthesis calibration [START_REF] Serrancoli | Neuromusculoskeletal Model Calibration Significantly Affects Predicted Knee Contact Forces for Walking[END_REF][START_REF] Walter | Muscle Synergies May Improve Optimization Prediction of Knee Contact Forces During Walking[END_REF].

Validation datasets

Among the 24 selected studies, 19 were interested in particular in the tibiofemoral joint, and 5 in the hip joint. The validation datasets used in these studies are directly related to the joint of interest.

In most of the selected studies, in vivo measurements of hip contact forces were obtained from the HIP98 dataset [START_REF] Bergmann | Hip contact forces and gait patterns from routine activities[END_REF], while tibiofemoral joint from the six editions of the Grand Challenge Competition to Predict In Vivo Knee Loads [START_REF] Fregly | Grand challenge competition to predict in vivo knee loads[END_REF]. In one study, the HIP98 dataset was pooled into a "typical patient" [START_REF] Heller | Determination of muscle loading at the hip joint for use in pre-clinical testing[END_REF]. In two studies [START_REF] Lin | Simultaneous prediction of muscle and contact forces in the knee during gait[END_REF][START_REF] Lundberg | Direct comparison of measured and calculated total knee replacement force envelopes during walking in the presence of normal and abnormal gait patterns[END_REF], the datasets associated with the studies published by Zhao et al. [START_REF] Zhao | In Vivo Medial and Lateral Tibial Loads during Dynamic and High Flexion Activities Dong[END_REF] and Mündermann et al. [START_REF] Mündermann | In vivo knee loading characteristics during activities of daily living as measured by an instrumented total knee replacement[END_REF] were used.

Alterations of the generic musculoskeletal model

Three types of alterations of the generic musculoskeletal model have been proposed in the selected studies: 1) skeletal and joint models, 2) muscle model, and 3) optimisation problem.

Details are given below.

Skeletal and joint models

Alterations of the skeletal and joint models concern kinematics and/or dynamics, and can be divided in nine sub-group: markers' placement [START_REF] Lund | Scaling of musculoskeletal models from static and dynamic trials[END_REF][START_REF] Navacchia | Prediction of In Vivo Knee Joint Loads Using a Global Probabilistic Analysis[END_REF], number of DoFs [START_REF] Dumas | Influence of joint models on lower-limb musculo-tendon forces and three-dimensional joint reaction forces during gait[END_REF][START_REF] Lundberg | Fine tuning total knee replacement contact force prediction algorithms using blinded model validation[END_REF], knee alignment [START_REF] Lerner | How tibiofemoral alignment and contact locations affect predictions of medial and lateral tibiofemoral contact forces[END_REF][START_REF] Navacchia | Prediction of In Vivo Knee Joint Loads Using a Global Probabilistic Analysis[END_REF][START_REF] Thelen | Co-Simulation of Neuromuscular Dynamics and Knee Mechanics During Human Walking[END_REF], femoral anteversion [START_REF] Heller | Influence of femoral anteversion on proximal femoral loading: measurement and simulation in four patients[END_REF], scaling strategy [START_REF] Chen | Prediction of in vivo joint mechanics of an artificial knee implant using rigid multi-body dynamics with elastic contacts[END_REF][START_REF] Lund | Scaling of musculoskeletal models from static and dynamic trials[END_REF], medial-lateral tibiofemoral contact forces ratio [START_REF] Lundberg | Fine tuning total knee replacement contact force prediction algorithms using blinded model validation[END_REF], inertial parameters [START_REF] Navacchia | Prediction of In Vivo Knee Joint Loads Using a Global Probabilistic Analysis[END_REF], contact points [START_REF] Lerner | How tibiofemoral alignment and contact locations affect predictions of medial and lateral tibiofemoral contact forces[END_REF][START_REF] Manal | An electromyogram-driven musculoskeletal model of the knee to predict in vivo joint contact forces during normal and novel gait patterns[END_REF], contact stiffness [START_REF] Chen | Prediction of in vivo joint mechanics of an artificial knee implant using rigid multi-body dynamics with elastic contacts[END_REF][START_REF] Zhang | Prediction of hip joint load and translation using musculoskeletal modelling with force-dependent kinematics and experimental validation[END_REF], and joint passive stiffness [START_REF] Dumas | Influence of joint models on lower-limb musculo-tendon forces and three-dimensional joint reaction forces during gait[END_REF][START_REF] Lundberg | Fine tuning total knee replacement contact force prediction algorithms using blinded model validation[END_REF].

Muscle model

Several studies also altered the muscle model by introducing some variations in: muscle geometry (i.e. insertion sites, muscle path) [START_REF] Navacchia | Prediction of In Vivo Knee Joint Loads Using a Global Probabilistic Analysis[END_REF][START_REF] Zhang | Prediction of hip joint load and translation using musculoskeletal modelling with force-dependent kinematics and experimental validation[END_REF], Hilltype muscle parameters (e.g. optimal fibre length, tendon slack length, pennation angle, EMG-driven model parameters) [START_REF] Manal | An electromyogram-driven musculoskeletal model of the knee to predict in vivo joint contact forces during normal and novel gait patterns[END_REF][START_REF] Navacchia | Prediction of In Vivo Knee Joint Loads Using a Global Probabilistic Analysis[END_REF][START_REF] Serrancoli | Neuromusculoskeletal Model Calibration Significantly Affects Predicted Knee Contact Forces for Walking[END_REF], or level of muscular redundancy (i.e. number of lines of action) [START_REF] Heller | Determination of muscle loading at the hip joint for use in pre-clinical testing[END_REF][START_REF] Moissenet | Influence of the level of muscular redundancy on the validity of a musculoskeletal model[END_REF].

Optimisation problem

Five studies altered the objective function of the optimisation problem by modifying the objective function [START_REF] Chen | Prediction of in vivo joint mechanics of an artificial knee implant using rigid multi-body dynamics with elastic contacts[END_REF][START_REF] Modenese | An open source lower limb model: Hip joint validation[END_REF][START_REF] Zhang | Prediction of hip joint load and translation using musculoskeletal modelling with force-dependent kinematics and experimental validation[END_REF], or the optimisation weights [START_REF] Knarr | Practical approach to subject-specific estimation of knee joint contact force[END_REF][START_REF] Steele | Compressive tibiofemoral force during crouch gait[END_REF]. Four studies altered the design variables by introducing in the optimisation problem: joint contact forces [START_REF] Demers | Changes in Tibiofemoral Forces due to Variations in Muscle Activity during Walking[END_REF][START_REF] Lin | Simultaneous prediction of muscle and contact forces in the knee during gait[END_REF][START_REF] Modenese | Application of a falsification strategy to a musculoskeletal model of the lower limb and accuracy of the predicted hip contact force vector[END_REF][START_REF] Moissenet | A 3D lower limb musculoskeletal model for simultaneous estimation of musculo-tendon, joint contact, ligament and bone forces during gait[END_REF], ligament and bone forces [START_REF] Moissenet | A 3D lower limb musculoskeletal model for simultaneous estimation of musculo-tendon, joint contact, ligament and bone forces during gait[END_REF], or reserve actuators [START_REF] Modenese | Application of a falsification strategy to a musculoskeletal model of the lower limb and accuracy of the predicted hip contact force vector[END_REF] instead of only muscle activations or musculo-tendon forces. Three studies altered the optimisation constraints by modifying the boundaries of design variables [START_REF] Hast | Dual-joint modeling for estimation of total knee replacement contact forces during locomotion[END_REF][START_REF] Knarr | Practical approach to subject-specific estimation of knee joint contact force[END_REF] or by introducing additional constraints (e.g. residual loads acting on the tibia) [START_REF] Lin | Simultaneous prediction of muscle and contact forces in the knee during gait[END_REF]. Other cases concerned the alteration of PID gains in a forward dynamics-based optimisation [START_REF] Guess | Concurrent Prediction of Muscle and Tibiofemoral Contact Forces During Treadmill Gait[END_REF] and the use of EMG synergies [START_REF] Walter | Muscle Synergies May Improve Optimization Prediction of Knee Contact Forces During Walking[END_REF].

Level of evidence

All the alterations proposed in the selected studies had an impact on joint contact force accuracy. However, the present review did not allow to highlight any trend defining which alteration has the largest potential to improve accuracy. Moreover, the evidence of this impact highly varied between studies, and could be divided in four categories. The first category corresponded to studies providing quantitative evidence with statistics (e.g. t-test, ANOVA, 95% confidence interval, Monte Carlo simulation) [START_REF] Lerner | How tibiofemoral alignment and contact locations affect predictions of medial and lateral tibiofemoral contact forces[END_REF][START_REF] Lund | Scaling of musculoskeletal models from static and dynamic trials[END_REF][START_REF] Serrancoli | Neuromusculoskeletal Model Calibration Significantly Affects Predicted Knee Contact Forces for Walking[END_REF]. In these studies, results related to accuracy were reported using the root mean square errors (RMSE) between the measured and estimated joint contact forces, expressed in Newton or in body weight (BW) (to ease comparison, all data have been expressed in BW in Table 1). The second category corresponded to studies providing quantitative evidence with only descriptive statistics [START_REF] Demers | Changes in Tibiofemoral Forces due to Variations in Muscle Activity during Walking[END_REF][START_REF] Heller | Determination of muscle loading at the hip joint for use in pre-clinical testing[END_REF][START_REF] Heller | Influence of femoral anteversion on proximal femoral loading: measurement and simulation in four patients[END_REF][START_REF] Knarr | Practical approach to subject-specific estimation of knee joint contact force[END_REF][START_REF] Lin | Simultaneous prediction of muscle and contact forces in the knee during gait[END_REF][START_REF] Lundberg | Fine tuning total knee replacement contact force prediction algorithms using blinded model validation[END_REF][START_REF] Lundberg | Direct comparison of measured and calculated total knee replacement force envelopes during walking in the presence of normal and abnormal gait patterns[END_REF][START_REF] Manal | An electromyogram-driven musculoskeletal model of the knee to predict in vivo joint contact forces during normal and novel gait patterns[END_REF][START_REF] Modenese | Application of a falsification strategy to a musculoskeletal model of the lower limb and accuracy of the predicted hip contact force vector[END_REF][START_REF] Modenese | An open source lower limb model: Hip joint validation[END_REF][START_REF] Moissenet | Influence of the level of muscular redundancy on the validity of a musculoskeletal model[END_REF], 2014;[START_REF] Walter | Muscle Synergies May Improve Optimization Prediction of Knee Contact Forces During Walking[END_REF]. In most of these studies, results related to accuracy were reported using the root mean square error (RMSE) between the measured and estimated joint contact forces. The third category corresponded to studies providing only a partial quantitative evidence. Two cases could be listed in this category. Firstly, only RMSE between baseline and altered joint contact forces was given [START_REF] Chen | Prediction of in vivo joint mechanics of an artificial knee implant using rigid multi-body dynamics with elastic contacts[END_REF][START_REF] Zhang | Prediction of hip joint load and translation using musculoskeletal modelling with force-dependent kinematics and experimental validation[END_REF]. Secondly, only quantitative evidence on accuracy at baseline or after alterations was given [START_REF] Navacchia | Prediction of In Vivo Knee Joint Loads Using a Global Probabilistic Analysis[END_REF][START_REF] Steele | Compressive tibiofemoral force during crouch gait[END_REF][START_REF] Thelen | Co-Simulation of Neuromuscular Dynamics and Knee Mechanics During Human Walking[END_REF]. In both cases, the results highlighted the alteration impact on accuracy, but do not quantify it. The last category corresponded to studies providing no quantitative evidence [START_REF] Dumas | Influence of joint models on lower-limb musculo-tendon forces and three-dimensional joint reaction forces during gait[END_REF][START_REF] Guess | Concurrent Prediction of Muscle and Tibiofemoral Contact Forces During Treadmill Gait[END_REF][START_REF] Hast | Dual-joint modeling for estimation of total knee replacement contact forces during locomotion[END_REF]. In this case, results related to accuracy were presented in figures, leading to an approximate interpretation of accuracy.

Impact of the alterations on joint contact force accuracy

On the whole, by merging all reported quantitative data before alteration (16 out 24 studies), the range of RMSE at baseline (between estimated and measured joint contact forces) extracted from the selected studies providing this value was 0.17 -1.39 BW for the tibiofemoral medial contact force, 0.18 -0.81 BW for the tibiofemoral lateral contact force, 0.30 -0.88 BW for the tibiofemoral total contact force, and 0.45 -0.59 BW for the hip contact force (Figure 2 and Figure 3: some ranges may differ with these figures where only studies providing baseline and alterations RMSE were reported). After alterations (13 out of 24 studies), the range of RMSE was 0.08 -0.50 BW for the tibiofemoral medial contact force, 0.09 -0.63 BW for the tibiofemoral lateral contact force, 0.15 -0.77 BW for the tibiofemoral total contact force, and 0.18 -4.52 BW for the hip contact force (Figure 2 and Figure 3). At the hip joint, the use of high power in the objective function had a negative impact on the upper bound of accuracy (Figure 3). At the knee joint, instead, both the minimum and maximum RMSE were decreased for each evaluated contact force component. Moreover, unsurprisingly, the minimum RMSE was obtained when tracking the measured tibiofemoral joint contact forces [START_REF] Serrancoli | Neuromusculoskeletal Model Calibration Significantly Affects Predicted Knee Contact Forces for Walking[END_REF][START_REF] Walter | Muscle Synergies May Improve Optimization Prediction of Knee Contact Forces During Walking[END_REF] by reaching a value of 0.08 BW, 0.09 BW, and 0.15 BW, respectively for the tibiofemoral medial, lateral and total contact forces (Figure 2). It must also be noticed that some alterations mainly have an impact on the medial-lateral tibiofemoral contact forces ratio, with only slight variations on the total contact force accuracy [START_REF] Moissenet | Influence of the level of muscular redundancy on the validity of a musculoskeletal model[END_REF][START_REF] Thelen | Co-Simulation of Neuromuscular Dynamics and Knee Mechanics During Human Walking[END_REF].

Discussion

Accuracy assessment

The way the joint contact force accuracy was assessed varies between studies, leading to different levels of interpretation. Firstly, the alterations could be simply applied [START_REF] Demers | Changes in Tibiofemoral Forces due to Variations in Muscle Activity during Walking[END_REF][START_REF] Dumas | Influence of joint models on lower-limb musculo-tendon forces and three-dimensional joint reaction forces during gait[END_REF][START_REF] Guess | Concurrent Prediction of Muscle and Tibiofemoral Contact Forces During Treadmill Gait[END_REF][START_REF] Hast | Dual-joint modeling for estimation of total knee replacement contact forces during locomotion[END_REF][START_REF] Heller | Determination of muscle loading at the hip joint for use in pre-clinical testing[END_REF][START_REF] Heller | Influence of femoral anteversion on proximal femoral loading: measurement and simulation in four patients[END_REF][START_REF] Knarr | Practical approach to subject-specific estimation of knee joint contact force[END_REF][START_REF] Lerner | How tibiofemoral alignment and contact locations affect predictions of medial and lateral tibiofemoral contact forces[END_REF][START_REF] Lin | Simultaneous prediction of muscle and contact forces in the knee during gait[END_REF][START_REF] Lund | Scaling of musculoskeletal models from static and dynamic trials[END_REF][START_REF] Lundberg | Fine tuning total knee replacement contact force prediction algorithms using blinded model validation[END_REF][START_REF] Manal | An electromyogram-driven musculoskeletal model of the knee to predict in vivo joint contact forces during normal and novel gait patterns[END_REF][START_REF] Modenese | Application of a falsification strategy to a musculoskeletal model of the lower limb and accuracy of the predicted hip contact force vector[END_REF][START_REF] Modenese | An open source lower limb model: Hip joint validation[END_REF][START_REF] Moissenet | Influence of the level of muscular redundancy on the validity of a musculoskeletal model[END_REF], 2014;[START_REF] Serrancoli | Neuromusculoskeletal Model Calibration Significantly Affects Predicted Knee Contact Forces for Walking[END_REF][START_REF] Steele | Compressive tibiofemoral force during crouch gait[END_REF][START_REF] Thelen | Co-Simulation of Neuromuscular Dynamics and Knee Mechanics During Human Walking[END_REF][START_REF] Walter | Muscle Synergies May Improve Optimization Prediction of Knee Contact Forces During Walking[END_REF] or explored through a sensitivity analysis [START_REF] Chen | Prediction of in vivo joint mechanics of an artificial knee implant using rigid multi-body dynamics with elastic contacts[END_REF][START_REF] Lundberg | Direct comparison of measured and calculated total knee replacement force envelopes during walking in the presence of normal and abnormal gait patterns[END_REF][START_REF] Navacchia | Prediction of In Vivo Knee Joint Loads Using a Global Probabilistic Analysis[END_REF][START_REF] Zhang | Prediction of hip joint load and translation using musculoskeletal modelling with force-dependent kinematics and experimental validation[END_REF]. Secondly, it has been showed that the provided level of evidence varied between studies. This was mainly due to differences in the generic musculoskeletal model and in the type of alteration. Indeed, results clearly show that the baseline situation of most of the studies is different, with various modelling approaches (e.g. computation of joint contact forces based on one-point rigid contact model versus deformable contact with viscous damping model) and different levels of details (e.g. number lines of action varying between 11 and 163). Thirdly, the units of the error metric used to report joint contact forces accuracy also differed between studies (e.g. Newton, BW, percents). Even if RMSE expressed in BW has been used in several works, a consensual methodology would have eased the comparison.

Strategies of alteration

With some exceptions, most of the strategies of alteration could be gathered in two categories in the selected studies.

On one hand, alterations could have a substantial impact on the model lever arms. This could be done by tuning the position and orientation of joint centres and segmental coordinate systems (e.g. indirectly through markers' placement [START_REF] Lund | Scaling of musculoskeletal models from static and dynamic trials[END_REF][START_REF] Navacchia | Prediction of In Vivo Knee Joint Loads Using a Global Probabilistic Analysis[END_REF]). Another option was to modify the position of the contact points by tuning the knee alignment [START_REF] Lerner | How tibiofemoral alignment and contact locations affect predictions of medial and lateral tibiofemoral contact forces[END_REF][START_REF] Navacchia | Prediction of In Vivo Knee Joint Loads Using a Global Probabilistic Analysis[END_REF][START_REF] Thelen | Co-Simulation of Neuromuscular Dynamics and Knee Mechanics During Human Walking[END_REF], or the mediallateral position of contact points [START_REF] Lerner | How tibiofemoral alignment and contact locations affect predictions of medial and lateral tibiofemoral contact forces[END_REF][START_REF] Manal | An electromyogram-driven musculoskeletal model of the knee to predict in vivo joint contact forces during normal and novel gait patterns[END_REF]. In the context of a multi-contact joint (e.g. tibiofemoral joint), the lever arms of joint contact forces was then modified, and thus the repartition of forces between contact points. A third possibility was to modify the muscular geometry by tuning the skeletal geometry (e.g. femoral anteversion [START_REF] Heller | Influence of femoral anteversion on proximal femoral loading: measurement and simulation in four patients[END_REF]), or directly the muscular geometry (e.g. insertion sites, muscle path [START_REF] Navacchia | Prediction of In Vivo Knee Joint Loads Using a Global Probabilistic Analysis[END_REF][START_REF] Zhang | Prediction of hip joint load and translation using musculoskeletal modelling with force-dependent kinematics and experimental validation[END_REF]). The muscular lever arms were then directly affected, leading to new estimations of musculo-tendon forces and thus joint contact forces.

On the other hand, alterations could have a substantial impact on the distribution of musculotendon forces (i.e. on the solution of the muscular redundancy problem). This could first be done by modifying the number of DoFs [START_REF] Dumas | Influence of joint models on lower-limb musculo-tendon forces and three-dimensional joint reaction forces during gait[END_REF][START_REF] Lundberg | Fine tuning total knee replacement contact force prediction algorithms using blinded model validation[END_REF]. Indeed, increasing the number of DoFs may results in the recruitment of muscle aiming to stabilise the altered joint [START_REF] Jinha | Predictions of co-contraction depend critically on degrees-of-freedom in the musculoskeletal model[END_REF]. The sum of musculo-tendon forces related to this joint may thus be increased, leading to a higher joint contact force [START_REF] Dumas | Influence of joint models on lower-limb musculo-tendon forces and three-dimensional joint reaction forces during gait[END_REF]. The muscular redundancy also played a key role in the distribution of musculo-tendon forces [START_REF] Heller | Determination of muscle loading at the hip joint for use in pre-clinical testing[END_REF][START_REF] Moissenet | Influence of the level of muscular redundancy on the validity of a musculoskeletal model[END_REF]. By increasing the number of muscular lines of action, the load may be shared by several musculo-tendon units with different lever arms, and thus impact joint contact forces. Finally, the way the optimisation problem was defined had a substantial impact on the force sharing problem. By altering the objective function [START_REF] Chen | Prediction of in vivo joint mechanics of an artificial knee implant using rigid multi-body dynamics with elastic contacts[END_REF][START_REF] Knarr | Practical approach to subject-specific estimation of knee joint contact force[END_REF][START_REF] Modenese | An open source lower limb model: Hip joint validation[END_REF][START_REF] Steele | Compressive tibiofemoral force during crouch gait[END_REF][START_REF] Zhang | Prediction of hip joint load and translation using musculoskeletal modelling with force-dependent kinematics and experimental validation[END_REF], the design variables [START_REF] Demers | Changes in Tibiofemoral Forces due to Variations in Muscle Activity during Walking[END_REF][START_REF] Lin | Simultaneous prediction of muscle and contact forces in the knee during gait[END_REF][START_REF] Modenese | Application of a falsification strategy to a musculoskeletal model of the lower limb and accuracy of the predicted hip contact force vector[END_REF][START_REF] Moissenet | A 3D lower limb musculoskeletal model for simultaneous estimation of musculo-tendon, joint contact, ligament and bone forces during gait[END_REF], or the optimisation constraints [START_REF] Hast | Dual-joint modeling for estimation of total knee replacement contact forces during locomotion[END_REF][START_REF] Knarr | Practical approach to subject-specific estimation of knee joint contact force[END_REF][START_REF] Lin | Simultaneous prediction of muscle and contact forces in the knee during gait[END_REF], the way the muscles were recruited varied and generated different musculotendon forces amplitudes and patterns. This could be explained by the fact that these alterations modified the solution space of the optimisation problem, allowing the identification of new optimal solutions.

Limitations

The present systematic review was limited to studies exploring only lower limb musculoskeletal models during normal gait, where joint contact estimations were validated against instrumented prosthesis measurements. Several types of musculoskeletal model may thus have been omitted. This was for example the case for finite element models (e.g. [START_REF] Adouni | Role of gastrocnemius activation in knee joint biomechanics: gastrocnemius acts as an ACL antagonist[END_REF][START_REF] Beillas | A new method to investigate in vivo knee behavior using a finite element model of the lower limb[END_REF][START_REF] Marouane | 3D active-passive response of human knee joint in gait is markedly altered when simulated as a planar 2D joint[END_REF][START_REF] Marouane | Alterations in knee contact forces and centers in stance phase of gait: A detailed lower extremity musculoskeletal model[END_REF] for whose no study was found with validation against instrumented prosthesis measurements. Similarly, several alterations may thus have been omitted. This was for example the case for the study of Smith et al. [START_REF] Smith | The Influence of Component Alignment and Ligament Properties on Tibiofemoral Contact Forces in Total Knee Replacement[END_REF], studying modified styles of gait (i.e. smooth and bouncy), where a variant of the Arnold et al. model (Arnold et al., 2010) was used in an inverse dynamics-based optimisation to evaluate the impact of alterations on knee alignment and knee ligament stiffness on tibiofemoral joint contact forces accuracy. It was also interesting to note that the studies using subject-specific musculoskeletal models, excluded from this review, specifically personalised the following items: the bone geometry and the origin and insertion of muscle line of actions [START_REF] Ding | In Vivo Knee Contact Force Prediction Using Patient-Specific Musculoskeletal Geometry in a Segment-Based Computational Model[END_REF][START_REF] Gerus | Subject-specific knee joint geometry improves predictions of medial tibiofemoral contact forces[END_REF][START_REF] Jung | Intra-articular Knee Contact Force Estimation during Walking using Force-reaction Elements and Subject-specific Joint Model[END_REF][START_REF] Kia | Evaluation of a Musculoskeletal Model with Prosthetic Knee through Six Experimental Gait Trials[END_REF][START_REF] Marra | A Subject-Specific Musculoskeletal Modeling Framework to Predict in Vivo Mechanics of Total Knee Arthroplasty[END_REF], the joint kinematics (i.e. coupling curves between de DoFs) [START_REF] Gerus | Subject-specific knee joint geometry improves predictions of medial tibiofemoral contact forces[END_REF], and the joint geometry (i.e. prosthesis design) when deformable joint was introduced [START_REF] Jung | Intra-articular Knee Contact Force Estimation during Walking using Force-reaction Elements and Subject-specific Joint Model[END_REF][START_REF] Kia | Evaluation of a Musculoskeletal Model with Prosthetic Knee through Six Experimental Gait Trials[END_REF][START_REF] Marra | A Subject-Specific Musculoskeletal Modeling Framework to Predict in Vivo Mechanics of Total Knee Arthroplasty[END_REF].

Conclusion

Musculoskeletal modelling is a computational methodology which involves determination of a large number of parameters and several implementation choices. With the objective of a more accurate estimation of the joint contact forces, some of these parameters, typically defining the joint and muscle geometries, have been personalised using medical imaging.

According to this systematic review, many of these model parameters and implementation choices have been also altered from a generic musculoskeletal model, e.g. number of degrees of freedom, muscle parameters, level of muscular redundancy, objective function, constraints, etc. All these alterations had an impact on the accuracy of the hip and tibiofemoral joint contact forces, so demonstrating the potential for improving model prediction without necessarily involving costly and time consuming medical images. However, due to discrepancies in the reported evidence about this impact and despite a high quality of the reviewed studies, it was not possible to highlight any trend defining which alteration had the largest impact. If results of future studies implementing model alterations will be reported with consistent metrics and level of evidence, it might be possible to identify specific

Tables and figures captions

Table 1: Overview of selected studies including generic musculoskeletal model, validation dataset, model alterations, available statistics (other than descriptive), and impact of the alterations on joint contact force accuracy. 1 for the numbering of the alterations.

Figure 3: Mean errors on hip contact forces between baseline and alterations. See Table 1 for the numbering of the alterations.

Figure 1 :

 1 Figure 1: Flowchart of the search strategy conducted in this review.

Figure 2 :

 2 Figure 2: Mean errors on tibiofemoral medial, lateral and total contact forces between

Figure 1 Figure 3

 13 Figure 1

Table 1 (continued)

 1 An increase of muscular redundancy modified medial-lateral contact forces balance but did not impact the accuracy of the model to estimate total contact force Quantitative evidence: RMSE of medial, lateral, and total contact forces was (1) 0.65 BW, 0.45 BW, and 0.70 BW, (2) 0.26 BW, 0.63 BW, and 0.77 BW Variant of the[START_REF] Delp | An interactive graphics-based model of the lower extremity to study orthopeadic surgery procedures[END_REF] model DoFs: 6 pelvis, 3 hip, 2 TF, 1 ankle Muscular lines of action: 92 per leg Muscle path: Via points Optimisation: Inverse dynamics-based optimisation Objective function: Sum of squared muscle activations Joint contact model: One-point rigid contact model (Steele et al., 2012) Variant of the Arnold et al. (2010) model DoFs: 6 pelvis, 3 hip, 6 TF, 6 PF, 2 ankle Muscular lines of action: 44 per leg Muscle path: Via points and wrapping surfaces Optimisation: Inverse dynamics-based optimisation Objective function: Sum of squared muscle activations plus sum of six squared reserve activations Joint contact model: Validated regression equation converting superior-inferior force and varus-valgus moment to medial and lateral contact forces (Fregly et al., 2012) RMSE of the medial, lateral and total contact forces averaged across trials was (1) 0.51 BW, 0.55 BW, and 0.88 BW, (2) 0.08 BW, 0.09 BW, and 0.15 BW Statistics: Two-tailed t-test were used to evaluate statistical significant differences in results

	Article	Joint	Generic musculoskeletal model	Validation dataset	Model alterations	Impact of the alterations on joint contact force
						accuracy
	Moissenet	TF	Software: Matlab	First, second, third and	Optimisation problem:	Conclusion: The introduction of joint contact,
	et al.		Model: Variant of the Delp et al. (1990) model	fourth edition data of	-Design variables:	ligament, and bone forces in the muscular redundancy
	(2014)		DoFs: 6 pelvis, 3 hip, 1 TF, 1 PF, 1 ankle Muscular lines of action: 43 per leg	the Grand Challenge Competition to Predict	(1) Musculo-tendon forces (baseline)	problem improved model accuracy Quantitative evidence: RMSE ranges of medial and
			Muscle path: Via points	In Vivo Knee Loads	(2) Musculo-tendon	lateral contact forces across subjects were (1) 0.91-
			Optimisation: Inverse dynamics-based optimisation	(Fregly et al., 2012)	forces and joint	1.39 BW and 0.44-0.81 BW, (2) 0.31-0.50 BW and
			Objective function: Sum of squared musculo-tendon forces		contact, ligament, and	0.22-0.43 BW
			Joint contact model: Two-point rigid contact (Feikes et al., 2003)		bone forces	Statistics: None
	Moissenet et al. (2016)	TF	Software: Matlab Model: Variant of the Delp et al. (1990) model DoFs: 6 pelvis, 3 hip, 1 TF, 1 PF, 1 ankle Muscular lines of action: 43 per leg Muscle path: Via points Optimisation: Inverse dynamics-based optimisation Objective function: Sum of squared and weighted musculo-tendon forces and joint contact, ligament, and bone forces	Sixth edition data of the Grand Challenge Competition to Predict In Vivo Knee Loads (Fregly et al., 2012)	Muscle model: -Muscular redundancy: (1) 43 lines of action (baseline) (2) 163 lines of action (variant of the Klein Horsman et al. (2007) model)	Conclusion: Statistics: None
			Joint contact model: Two-point rigid contact (Feikes et al., 2003)
	Navacchia et al.	TF	Software: Opensim Model: Data of three undefined subjects of the Grand	Joint model: -Markers' placement	Conclusion: Each alteration impacted the model accuracy
	(2016)			Challenge Competition to Predict In Vivo	-Knee alignment -Inertial parameters	Quantitative evidence: At baseline, RMSE of the total contact force ranged between 0.30 BW and 0.35 BW.
				Knee Loads (Fregly et	Muscle model:	After alteration, no quantitative measurement of
				al., 2012)	-Muscle geometry	model accuracy is reported
					-Muscle parameters	Statistics: A Monte-Carlo simulation was conducted.
					(alterations performed	The validation data were within the predicted 5-95%
					under a sensitivity	confidence bounds for 77%, 83%, and 75% of stance
					analysis)	phase, respectively.
	Serrancoli et al.	TF	Software: Opensim Model: Fourth edition data of the Grand Challenge	Muscle model: -Muscle geometry	Conclusion: Tracking validation data improved model accuracy
	(2016)			In Vivo Knee Loads Competition to Predict	Variations are managed -Muscle parameters	Quantitative evidence:
				(Fregly et al., 2012)	though a two-level
					optimisation:
					(1) Without tracking
					validation data
					(baseline)
					(2) By tracking
					validation data

modelling features that, if personalised, will lead to an increased accuracy in the estimation of joint contact forces in the lower limb.

Conflict of interest

The authors declare that the study did not raise any conflict of interest.