Estimation des forces musculaires du membre supérieur humain par optimisation dynamique en utilisant une méthode directe de tir multiple

Musculoskeletal modeling is used to estimate the internal forces of the human body, namely, muscle and joint forces. These estimates are necessary to understand functional anatomy and pathogenesis or to design technical devices supporting the movement. The challenge is to use all existing biomechanical data to predict internal forces that account for the neuro-musculoskeletal strategies of each individual. The purpose of this thesis was to estimate the human upper-limb muscles forces using forward dynamic optimisation. To do so, we proposed an innovative method tracking both electromyographic (EMG) and kinematic data directly into the optimisation objective-function. In this regard, our four specific objectives were: (1) solving the forward-dynamic optimisation problem using a direct multiple shooting method; (2) determining its relevance and performance compared to other existing algorithms in the literature; (3) validating its applicability to experimental data; and (4) characterizing techniques to identify the model muscle properties using the isokinetic dynamometer. In our different studies, we have demonstrated that, in a reasonable computation time (~ 1 hour), our new dynamic-optimisation method is able to predict the joint kinematics with an accuracy of about 5°. In addition, the muscle forces root-mean-square error was reduced by at least five times with our new method compared to static, hybrid, and dynamic optimisations based on least-activations/excitations objective-functions (muscle forces error of 18.45 ± 12.60 N with our new method vs. 85.10 ± 116.40 N with a traditional hybrid optimisation tracking the joint torques). Our new algorithm also proved to be efficient in identifying the muscle properties of a generic musculoskeletal model: in doing so, the error between the optimised muscle excitations and the experimental EMG was two time lower than the one obtained with static optimisation. Finally, regarding the calibration of the musculoskeletal model, we established that the experimental joint torque measurement at the shoulder using the isokinetic dynamometer was not suitable, especially during internal/external rotation movements of the shoulder. In fact, the flexion and abduction components of the shoulder torque measured by the isokinetic dynamometer are significantly underestimated (up to 94.9% compared to the resulting torque calculated from the three-dimensional forces at the hand and at the elbow, measured by six-axis force sensors). Therefore, this thesis has emphasized the importance of tracking both EMG and kinematics in dynamic optimisation, in order to make reliable estimations of the upper-limb muscle forces – specifically when high co-contraction occurs. Besides, recommendations were issued about calibrating the musculoskeletal model from the experimental torques measured with the isokinetic dynamometer. It will be possible to apply our innovative forward-dynamic optimisation method to pathological populations to increase understanding of the pathomechanics of human movement and better assist health professionals and their patients.

Keyphrases: Upper-limb, Musculoskeletal model, Forward-dynamic optimisation, Electromyography, Direct multiple shooting method, Multiple data tracking, Muscle parameters identification