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Estimation of Muscle Torques from EMG and Kinematics During Planar Arm Movements

机译:平面臂运动中EMG和运动学肌肉扭矩的估算

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Estimating the contributions of individual muscles to limb movements is crucial to our understanding of the organization of the motor system. In pathological conditions, characterizing the roles of the individual muscles may provide a basis for devising personalized treatments. Recently, myoproces-sors have been used to computationally alter muscles directions of action (`virtual biomechanics') in order to study the motor system's modular structure and adaptive capabilities in isometric conditions. As a step toward extending these studies to actual movements, here we describe a myoprocessor model which is capable for predicting the muscle forces during planar arm movements in real-time, from EMG and movement data. We use a polynomial model to account for multi-joint muscle geometry and a Hill-type muscle model to predict muscle forces during movements. We report on an experimental apparatus and a two-step parameter estimation procedure which involves both isometric force generation and actual movements. Preliminar results show a good reconstruction performance in isometric conditions. During movements, the reconstruction performance generally degradates, specially for shoulder torque, and is only acceptable for some movements.
机译:估算单个肌肉对肢体运动的贡献对于我们对电机系统组织的理解至关重要。在病理条件下,表征各个肌肉的角色可以为设计个性化治疗提供依据。最近,Myoproces-SORS已被用于计算地改变肌肉的行动方向(“虚拟生物力学”),以便在等轴测条件下研究电机系统的模块化结构和自适应功能。作为将这些研究扩展到实际运动的步骤,这里我们描述了一种能够在实时预测平面臂运动期间的肌肉力,从EMG和移动数据预测肌肉力。我们使用多项式模型来考虑多关节肌肉几何和山型肌肉模型,以预测运动期间的肌肉力。我们报告实验装置和两步参数估计过程,涉及等距力产生和实际运动。预级结果在等距条件下表现出良好的重建性能。在运动期间,重建性能通常降低,特别是肩部扭矩,并且仅适用于一些运动。

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