Journal
BIOSENSORS-BASEL
Volume 12, Issue 5, Pages -Publisher
MDPI
DOI: 10.3390/bios12050312
Keywords
differentiable physics; electromyography (EMG); musculoskeletal model; Hill-type muscle; exoskeleton; motor control; adjoint method; gradient; differential equation
Funding
- Ministry of Science and Technology of Taiwan [MOST 111-2622-8-A49-011-TE2, 111-2321-B-A49-005, 110-2622-E-A49A-501, 109-2221-E010-004-MY2]
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This study presents an EMG-based single-joint exoskeleton system that combines a differentiable continuous system with a dynamic musculoskeletal model, achieving precise control of the exoskeleton and good performance in assisting movement.
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.
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