A Differentiable Dynamic Model for Musculoskeletal Simulation and Exoskeleton Control

An exoskeleton, a wearable device, was designed based on the user's physical and cognitive interactions. The control of the exoskeleton used biomedical signals reflecting user intention as input and its algorithm calculated 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 subject and exoskeleton. Here we propose an EMG-based single-joint exoskeleton system, 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 by rate of convergence and execution time. In conclusion, a differentiable continuous system merged with a dynamic musculoskeletal model supported effective and accurate performance of an exoskeleton controlled by EMG signals.