Shoulder musculo-skeletal modelling: from muscle path refinement to optimal control based on direct multiple shooting

点击翻译按钮获取中文摘要

Shoulder disorder prevalence stands just after back pain. Due to shoulder muscle redundancy and the presence of polyarticular muscles, the contribution of each muscle to joint mobility versus stability is difficulty to determine without an estimate of muscle forces. Such analyses can contribute to improved diagnosis and treatment of neurological and orthopedic diseases. Since dynamic measure of muscle forces are unfeasible in clinical settings, non-invasive protocols with electromyography (EMG) and kinematics are to be combined with musculoskeletal (MSK) models to estimate muscle forces. Using this approach, we recently design an innovative shoulder post-operative orthosis. However, from our and other’s experience, current MSK models have be found to fail in more personalized clinical and industrial applications. Models are not subject-specific in terms of strength capacity and muscle coordination strategies. Furthermore, to date modeling efforts have not focused on the rotator cuff muscles, while the most frequent and costly shoulder complaints are related to these deep muscles, which run from the scapula to the humerus. Our 5-year objective is to identify personalized shoulder muscle parameters with a particular emphasis on rotator cuff muscles (specific objective, SO1) to estimate accurate muscle force in occupational tasks (SO2), using both EMG and kinematic data. Both optimal control and identification problems will be solved using a direct multiple shooting method. This method is well-tried in robotics and biomechanics but has never been applied to MSK models. A complementary objective (SO3) is to refine the rotator cuff geometry in the MSK model. SO1: To identify muscle parameters, subjects will perform series of isometric and isokinetic maximal voluntary contractions on a dynamometer. Diversity of exercises is essential since no single movement fully activates all shoulder muscles. SO2: In contrast to existing methods for estimating muscle forces, which assume noiseless kinematics, the novelty of our approach is to jointly optimize kinematic and EMG data, under the constraint that the optimal solution remains within intra-subject variability when repeating a standardized task. An expected error in joint kinematics will also be added to variability, since clinical protocols with markers put on the skin lead to substantial errors in shoulder bone kinematics. While in the last 3 years we focused on estimating of shoulder joint kinematics, the accuracy is still unknown. Skin marker-based kinematics will be compared to skeletal kinematics using a gold standard, namely pins screwed in clavicle, scapula and humerus. SO3: Finally, more physiological muscle paths should be obtained using a mesh of springs instead of independent lines of action that spread on the humeral head in a non-physiological manner. The mesh should reproduce the geometry of the tendons which interdigitate with each other to form a cuff around the humeral head. Cadaveric shoulders will be used to validate the model in several arm configurations. Our method will provide the best estimate of muscle forces for a subject-specific technique. This proposal is an outstanding opportunity to train 4 graduate students and 10 undergraduates in both experimental human biomechanics with the most advanced equipment and optimal control algorithms of multibody systems. Our method is expected to become the state-of-the-art in shoulder MSK modeling, particularly to study rotator cuff injuries. Refining simulation models and improving methods for shoulder optimal control are the cornerstone of projects leading to shoulder orthotic development, improvements in surgical restoration of shoulder muscle tears and in the prevention of shoulder injuries in the workplace.