运动机器人的目标肌肉力量分布：轨迹和阻力效应
Targeted Muscle Effort Distribution with Exercise Robots: Trajectory and Resistance Effects
摘要：
这项工作的目的是将肌肉的努力分布的轨迹和阻力设置的机器人运动和康复机器。肌肉的努力分布，代表在训练活动中的每一块肌肉的参与，用肌电图传感器（EMG）测量，并定义为单独的激活除以总的肌肉群激活。一个四自由度的机器人和它的阻抗控制系统被用来创建先进的锻炼协议，其中用户被要求遵循的路径对机器的中性路径和阻力。在这项工作中，机器人建立了一个零努力的圆形路径，并要求受试者遵循椭圆轨迹。控制系统在与中性路径的偏差和受试者施加的扭矩之间产生用户定义的刚度。实验中使用的轨迹和阻力设置是椭圆的方向和刚度参数。使用这些参数的多种组合来测量它们对肌肉努力分布的影响。人工神经网络（ANN）使用部分数据来训练模型。然后，使用其余数据评估模型的准确性。结果显示了模型的精度如何随着时间的推移而丢失。这些结果显示了长期估计的肌肉动力学的复杂性，表明存在可能与疲劳相关的时变动力学。
英文摘要：
The objective of this work is to relate muscle effort distributions to the trajectory and resistance settings of a robotic exercise and rehabilitation machine. Muscular effort distribution, representing the participation of each muscle in the training activity, was measured with electromyography sensors (EMG) and defined as the individual activation divided by the total muscle group activation. A four degrees-of-freedom robot and its impedance control system are used to create advanced exercise protocols whereby the user is asked to follow a path against the machine's neutral path and resistance. In this work, the robot establishes a zero-effort circular path, and the subject is asked to follow an elliptical trajectory. The control system produces a user-defined stiffness between the deviations from the neutral path and the torque applied by the subject. The trajectory and resistance settings used in the experiments were the orientation of the ellipse and a stiffness parameter. Multiple combinations of these parameters were used to measure their effects on the muscle effort distribution. An artificial neural network (ANN) used part of the data for training the model. Then, the accuracy of the model was evaluated using the rest of the data. The results show how the precision of the model is lost over time. These outcomes show the complexity of the muscle dynamics for long-term estimations suggesting the existence of time-varying dynamics possibly associated with fatigue.
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