Shoulder musculoskeletal modeling: from data-tracking to predictive simulations

我的长期目标是模拟生物逼真的最佳上肢运动，以改善肩部功能或减少肩部疾病的风险因素。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是必不可少的。这样的负荷可以使用神经肌肉骨骼[NMSK]模型来估计。然而，在NMSK建模中，肌肉冗余仍然是一个尚未解决的问题。我们方法的独创性是同时跟踪肌电和皮肤标记物来估计内部负荷。它不仅在数据跟踪模拟方面很有前途，而且在预测性模拟方面也很有前景(即在没有实验数据的情况下产生最佳的生物逼真运动)。然而，将技术转化为临床/人体工学应用的主要障碍仍然存在；主要障碍与我的特定目标相对应：SO1)使用在线NMSK数据跟踪模拟提供内部负荷的实时生物反馈；SO2)识别参与者特定的肌肉-肌腱属性；SO3)将数据跟踪算法转移到包含运动控制理论(肌肉协同效应和运动学理论)的预测模拟。*SO1)为了加快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具有更少变量的非线性移动时间跨度估计器(具有50-100 ms时间跨度)，具有增强的收敛能力。由于不是所有的肌电都可以系统地测量，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)中获取的数据来推断缺失的肌电。*为了个性化肌肉-肌腱特性，学生首先将重点放在利用等速测力仪上执行的一系列(次)最大努力来识别肌电和神经兴奋之间的最大等长肌力、最佳长度和非线性形状因素。来自系统生物学的识别算法将适用于NMSK模型。*SO3)肌肉协同效应将首先从我们的大型肌电数据库中提取。协同效应的使用将减少控制空间，并可能加强肌肉兴奋的生物逼真模式(例如，复制共同收缩以保持关节的稳定性)。此外，根据运动学理论对手的速度进行约束，以指导优化朝着现实解的方向进行。最优解决方案将使用先前收集的动作来验证，以确定最相关的目标函数、约束和运动控制理论来生成真实的动作。*我的发现计划计划将支持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高级肌肉骨骼生物力学建模培训。我们开创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. **