Abstract：
An exoskeleton, a wearable device, was designed based on the user\'s physical and cognitive interactions. The control of the exoskeleton uses biomedical signals reflecting the user intention as input, and its algorithm is calculated as an output to make the movement smooth. However, the process of transforming the input of biomedical signals, such as electromyography (EMG), into the output of adjusting the torque and angle of the exoskeleton is limited by a finite time lag and precision of trajectory prediction, which result in a mismatch between the subject and exoskeleton. Here, we propose an EMG-based single-joint exoskeleton system by merging a differentiable continuous system with a dynamic musculoskeletal model. The parameters of each muscle contraction were calculated and applied to the rigid exoskeleton system to predict the precise trajectory. The results revealed accurate torque and angle prediction for the knee exoskeleton and good performance of assistance during movement. Our method outperformed other models regarding the rate of convergence and execution time. In conclusion, a differentiable continuous system merged with a dynamic musculoskeletal model supported the effective and accurate performance of an exoskeleton controlled by EMG signals.
摘要：
一个外骨骼,可穿戴设备，是基于用户的身体和认知交互而设计的。外骨骼的控制使用反映用户意图的生物医学信号作为输入。并将其算法计算为输出以使运动平滑。然而,转换生物医学信号输入的过程，如肌电图（EMG），到输出调整的扭矩和角度的外骨骼是有限的时滞和精度的轨迹预测，这导致受试者和外骨骼之间的不匹配。这里,我们通过将可微分连续系统与动态肌肉骨骼模型合并，提出了基于EMG的单关节外骨骼系统。计算每个肌肉收缩的参数并将其应用于刚性外骨骼系统以预测精确轨迹。结果揭示了膝盖外骨骼的准确扭矩和角度预测以及运动期间的良好辅助性能。我们的方法在收敛速度和执行时间方面优于其他模型。总之,与动态肌肉骨骼模型合并的可微分连续系统支持由EMG信号控制的外骨骼的有效和准确的性能。
