Musculo-tendon forces and joint reaction forces are typically estimated using a two-step method, computing first the musculo-tendon forces by a static optimization procedure and then deducing the joint reaction forces from the force equilibrium. However, this method does not allow studying the interactions between musculo-tendon forces and joint reaction forces in establishing this equilibrium and the joint reaction forces are usually overestimated. This study introduces a new 3D lower limb musculoskeletal model based on a one-step static optimization procedure allowing simultaneous musculo-tendon, joint contact, ligament and bone forces estimation during gait. It is postulated that this approach, by giving access to the forces transmitted by these musculoskeletal structures at hip, tibiofemoral, patellofemoral and ankle joints, modeled using anatomically consistent kinematic models, should ease the validation of the model using joint contact forces measured with instrumented prostheses. A blinded validation based on four datasets was made under two different minimization conditions (i.e., C1 - only musculo-tendon forces are minimized, and C2 - musculo-tendon, joint contact, ligament and bone forces are minimized while focusing more specifically on tibiofemoral joint contacts). The results show that the model is able to estimate in most cases the correct timing of musculo-tendon forces during normal gait (i.e., the mean coefficient of active/inactive state concordance between estimated musculo-tendon force and measured EMG envelopes was C1: 65.87% and C2: 60.46%). The results also showed that the model is potentially able to well estimate joint contact, ligament and bone forces and more specifically medial (i.e., the mean RMSE between estimated joint contact force and in vivo measurement was C1: 1.14BW and C2: 0.39BW) and lateral (i.e., C1: 0.65BW and C2: 0.28BW) tibiofemoral contact forces during normal gait. However, the results remain highly influenced by the optimization weights that can bring to somewhat aphysiological musculo-tendon forces.

Human motion capture is used in various fields to analyse, understand and reproduce the diversity of movements that are required during daily-life activities. The proposed dataset of human gait has been established on 50 adults healthy and injury-free for lower and upper extremities in the most recent six months, with no lower and upper extremity surgery in the last two years. Participants were asked to walk on a straight level walkway at 5 speeds during one unique session: 0–0.4 m.s −1 , 0.4–0.8 m.s −1 , 0.8–1.2 m.s −1 , self-selected spontaneous and fast speeds. Three dimensional trajectories of 52 reflective markers spread over the whole body, 3D ground reaction forces and moment, and electromyographic signals were simultaneously recorded. For each participants, a minimum of 3 trials per condition have been made available in the dataset for a total of 1143 trials. This dataset could increase the sample size of similar datasets, lead to analyse the effect of walking speed on gait or conduct unusual analysis of gait thanks to the full body markerset used.

Soft tissue artefact (STA), i.e. the motion of the skin, fat and muscles gliding on the underlying bone, may lead to a marker position error reaching up to 8.7cm for the particular case of the scapula. Multibody kinematics optimisation (MKO) is one of the most efficient approaches used to reduce STA. It consists in minimising the distance between the positions of experimental markers on a subject skin and the simulated positions of the same markers embedded on a kinematic model. However, the efficiency of MKO directly relies on the chosen kinematic model. This paper proposes an overview of the different upper limb models available in the literature and a discussion about their applicability to MKO. The advantages of each joint model with respect to its biofidelity to functional anatomy are detailed both for the shoulder and the forearm areas. Models capabilities of personalisation and of adaptation to pathological cases are also discussed. Concerning model efficiency in terms of STA reduction in MKO algorithms, a lack of quantitative assessment in the literature is noted. In priority, future studies should concern the evaluation and quantification of STA reduction depending on upper limb joint constraints

Several three-dimensional (3D) lower-limb musculo-skeletal models have been developed for gait analysis and different hip, knee and ankle joint models have been considered in the literature. Conversely to the influence of the musculo-tendon geometry, the influence of the joint models--i.e. number of degrees of freedom and passive joint moments--on the estimated musculo-tendon forces and 3D joint reaction forces has not been extensively examined. In this paper musculo-tendon forces and 3D joint reaction forces have been estimated for one subject and one gait cycle with nine variations of a musculoskeletal model and outputs have been compared to measured electromyographic signals and knee joint contact forces. The model outputs are generally in line with the measured signals. However, the 3D joint reaction forces were higher than published values and the contact forces measured for the subject. The results of this study show that, with more degrees of freedom in the model, the musculo-tendon forces and the 3D joint reaction forces tend to increase but with some redistribution between the muscles. In addition, when taking into account passive joint moments, the 3D joint reaction forces tend to decrease during the stance phase and increase during the swing phase. Although further investigations are needed, a five-degree-of-freedom lower-limb musculo-skeletal model with some angle-dependent joint coupling and stiffness seems to provide satisfactory musculo-tendon forces and 3D joint reaction forces.