Cop Christopher P, Schouten Alfred C, Koopman Bart, Sartori Massimo
Department of Biomechanical Engineering, University of Twente, Enschede, The Netherlands.
Department of Biomechanical Engineering, University of Twente, Enschede, The Netherlands; Department of Biomechanical Engineering, Delft University of Technology, Delft, The Netherlands.
J Biomech. 2022 Dec;145:111383. doi: 10.1016/j.jbiomech.2022.111383. Epub 2022 Nov 10.
The simultaneous modulation of joint torque and stiffness enables humans to perform large repertoires of movements, while versatilely adapting to external mechanical demands. Multi-muscle force control is key for joint torque and stiffness modulation. However, the inability to directly measure muscle force in the intact moving human prevents understanding how muscle force causally links to joint torque and stiffness. Joint stiffness is predominantly estimated via joint perturbation-based experiments in combination with system identification techniques. However, these techniques provide joint-level stiffness estimations with no causal link to the underlying muscle forces. Moreover, the need for joint perturbations limits the generalizability and applicability to study natural movements. Here, we present an electromyography (EMG)-driven musculoskeletal modeling framework that can be calibrated to match reference joint torque and stiffness profiles simultaneously via a multi-term objective function. EMG-driven models calibrated on <2 s of reference torque and stiffness data could blindly estimate reference profiles across 100 s of data not used for calibration. Model calibrations using an objective function comprising torque and stiffness terms always provided less feasible solutions than an objective function comprising solely a torque term, thereby reducing the space of feasible muscle-tendon parameters. Results also showed the proposed framework's ability to estimate joint stiffness in unperturbed conditions, while capturing differences against stiffness profiles derived during perturbed conditions. The proposed framework may provide new ways for studying causal relationships between muscle force and joint torque and stiffness during movements in interaction with the environment, with broad implications across biomechanics, rehabilitation and robotics.
关节扭矩和刚度的同时调节使人类能够执行大量的动作，同时灵活地适应外部机械需求。多肌肉力量控制是关节扭矩和刚度调节的关键。然而，无法直接测量完整运动人体中的肌肉力量，这阻碍了我们理解肌肉力量与关节扭矩和刚度之间的因果关系。关节刚度主要通过基于关节扰动的实验结合系统识别技术来估计。然而，这些技术提供的是关节水平的刚度估计，与潜在的肌肉力量没有因果联系。此外，对关节扰动的需求限制了其在研究自然运动方面的通用性和适用性。在此，我们提出了一种肌电图（EMG）驱动的肌肉骨骼建模框架，该框架可以通过多目标函数进行校准，以同时匹配参考关节扭矩和刚度曲线。在<2秒的参考扭矩和刚度数据上校准的EMG驱动模型可以盲目估计100秒未用于校准的数据的参考曲线。使用包含扭矩和刚度项的目标函数进行模型校准，总是比仅包含扭矩项的目标函数提供更少的可行解，从而减少了可行的肌肉肌腱参数空间。结果还表明，所提出的框架能够在无扰动条件下估计关节刚度，同时捕捉与扰动条件下得出的刚度曲线的差异。所提出的框架可能为研究与环境相互作用时运动过程中肌肉力量与关节扭矩和刚度之间的因果关系提供新方法，在生物力学、康复和机器人技术等领域具有广泛的意义。
