Shoulder musculoskeletal modeling: from data-tracking to predictive simulations

我的长期目标是模拟生物力学和最佳上肢运动，以改善肩关节功能或减少肩关节疾病的风险因素。在生物力学中，内部载荷（肌肉肌腱和关节力）对于理解人类如何运动和预测功能结果至关重要。这种负荷可以使用神经肌肉骨骼[NMSK]模型进行估计。然而，肌肉冗余仍然是一个未解决的问题，在NMSK建模。我们的方法的独创性是同时跟踪EMG和皮肤标记来估计内部负荷。它不仅适用于数据跟踪模拟，而且适用于预测模拟（即在没有实验数据的情况下生成最佳和生物运动）。然而，将技术转化为临床/人体工程学应用的主要障碍仍然存在;主要障碍与我的具体目标相对应：SO 1）使用在线NMSK数据跟踪模拟提供内部负载的实时生物反馈; SO2）识别参与者特定的肌肉肌腱特性; SO 3）将数据跟踪算法转移到包括运动控制理论（肌肉协同作用和运动学理论）的预测模拟。 SO 1）为了加速优化过程并向患者和临床提供反馈，最优控制问题将被表示为非线性移动时域估计器（具有50-100 ms的时间跨度），由于变量更少而具有增强的收敛性。由于不是所有的EMG都可以系统地测量，因此将使用长短期记忆神经网络从执行各种任务的人群（n=30）的数据中推断缺失的EMG。 SO2）为了个性化肌肉肌腱特性，学生将首先专注于使用等速测力计上执行的一系列（子）最大努力来识别最大等长肌力、最佳长度和EMG与神经激励之间的非线性形状因子。系统生物学的识别算法将适用于NMSK模型。 SO 3）肌肉协同作用将首先从我们的大型EMG数据库中提取。协同作用的使用将减少控制空间，并可以强制肌肉兴奋的生物电模式（例如，复制盂肱关节稳定性的共同收缩）。此外，手的速度将根据运动学理论来约束，以引导优化向现实的解决方案。最佳解决方案将使用先前收集的运动进行验证，以确定最相关的目标函数，约束条件和运动控制理论，以生成逼真的运动。 我的发现计划提案将支持4名博士和10名本科生，他们将在多学科环境和最先进的基础设施中接受先进的肌肉骨骼生物力学建模培训。我们突破性的算法将成为临床、体育、艺术和人体工程学应用的基础。

My long-term objective is to simulate biofidelic and optimal upper-limb movements to improve shoulder function or reduce risk factors for shoulder disorders. In biomechanics, internal loads (muscle-tendon and joint forces) are essential for understanding how humans move and for predicting functional outcome. Such loads can be estimated using neuro-musculoskeletal [NMSK] models. However, muscle redundancy remains an unsolved problem in NMSK modeling. The originality of our approach has been to track simultaneously EMG and skin markers to estimate the internal loads. It is promising, not only for data-tracking simulations, but also for predictive simulations (i.e. generating optimal and biofidelic movements, without experimental data). However, major obstacles remain for technology translation to clinical/ergonomic applications; the major ones correspond to my specific objectives: SO1) Provide real-time biofeedback of internal loads using an online NMSK data-tracking simulation; SO2) Identify participant-specific muscle-tendon properties; SO3) Transfer data-tracking algorithm to predictive simulations with inclusion of motor control theories (muscle synergies and the kinematic theory). SO1) To speed-up the optimization process and provide feedback to patients and clinical, the optimal control problem will be expressed as a nonlinear moving horizon estimator (with 50-100 ms time span) with enhanced convergence due to fewer variables. Since not all EMGs can be systematically measured, missing EMG will be inferred using a long short-term memory neural network from data taken on a population (n=30) performing various tasks. SO2) To personalize muscle-tendon properties, students will first focus on the identification of maximal isometric muscle forces, optimal lengths, and nonlinear shape factors between EMG and neural excitation using series of (sub)maximal efforts performed on an isokinetic dynamometer. Identification algorithms from systems biology will be adapted to NMSK models. SO3) Muscle synergies will be first extracted from our large EMG database. Use of synergies will reduce the control space and could enforce biofidelic patterns of muscle excitations (e.g. to replicate the co-contraction for glenohumeral joint stability). Moreover, the velocity of the hand will be constrained according to the kinematic theory to guide the optimization toward realistic solutions. The optimal solutions will be validated using previously-collected movements to determine the most relevant objective functions, constraints and motor control theories for generating realistic movements. My Discovery Program proposal will support 4 PhD and 10 undergraduate students who will be trained on advanced musculoskeletal biomechanics modelling in a multidisciplinary environment and state-of-the art infrastructure. Our ground-breaking algorithms will be the foundation of clinical, sports, artistic and ergonomic applications.