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

肩部肌肉骨骼建模：从肌肉路径细化到基于直接多重射击的最优控制

• 批准号: RGPIN-2014-03912
• 负责人: Begon, Mickael
• 金额: $ 2.4万
• 依托单位: Université de Montréal
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=638479
• 关键词: Shoulder; musculo; skeletal; modelling; muscle

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.

肩部疾病的患病率仅次于背痛，由于肩部肌肉冗余和多关节肌肉的存在，如果不估计肌肉力量，就很难确定每块肌肉对关节活动性和稳定性的贡献。由于动态测量肌肉力量在临床环境中不可行，因此将肌电图（EMG）和运动学的非侵入性方案与肌肉骨骼（MSK）模型相结合来估计肌肉。使用这种方法，我们最近设计了一种创新的肩部术后矫形器，但是根据我们和其他人的经验，目前的 MSK 模型在更个性化的临床和工业应用中并不适用。此外，迄今为止，建模工作并未集中在肩袖肌肉上，而最常见且成本最高的肩部不适与这些从肩胛骨延伸到肱骨的深层肌肉有关。今年的目标是确定个性化肩部肌肉参数，特别强调肩袖肌肉（特定目标，SO1），以使用肌电图和运动学数据来估计职业任务中的准确肌肉力量（SO2），控制和识别问题都将使用直接多重方法来解决。这种方法在机器人学和生物力学中得到了充分的尝试，但从未应用于 MSK 模型。补充目标 (SO3) 是改进 MSK 模型中的肩袖几何形状。SO1：为了识别肌肉参数，受试者将执行。在测力计上进行一系列等长和等速最大自主收缩，练习的多样性至关重要，因为没有任何单一运动可以完全激活所有肩部肌肉。SO2：与假设无噪声运动学的现有估计肌肉力的方法相比，我们方法的新颖之处在于。联合优化运动学和肌电图数据，在重复标准化任务时最佳解决方案保持在受试者内部变异性的约束下，还将添加联合运动学的预期误差。可变性，因为在皮肤上放置标记的临床方案会导致肩骨运动学出现重大错误，虽然在过去 3 年中我们专注于肩关节运动学的估计，但与基于皮肤标记的运动学进行比较的准确性仍然未知。使用黄金标准的骨骼运动学，即拧入锁骨、肩胛骨和肱骨的销钉。SO3：最后，应该使用弹簧网而不是独立的动作线来获得更多的生理肌肉路径以非生理方式分布在肱骨头上的网格应再现相互交叉的肌腱的几何形状，以在尸体肩部周围形成袖带，以验证多种手臂配置的模型。我们的方法将为特定学科的技术提供肌肉力量的最佳估计。该提案是一个绝佳的机会，可以使用最先进的设备和多体最优控制算法来培训 4 名研究生和 10 名本科生实验人体生物力学我们的方法预计将成为肩部 MSK 建模的最先进方法，特别是研究肩袖损伤时，改进肩部最佳控制的模拟模型和改进方法是导致肩部矫形器开发和改进的项目的基石。用于肩部肌肉撕裂的手术修复和预防工作场所的肩部损伤。