Physics-Embedded Neural Networks for sEMG-based Continuous Motion Estimation
This addresses the challenge of myoelectric control for rehabilitation robotics and assistive technologies by providing a more accurate and physiologically consistent motion estimation method, though it appears incremental as it builds on existing approaches.
The paper tackled the problem of accurately decoding human motion from sEMG by introducing a Physics-Embedded Neural Network (PENN) that combines musculoskeletal forward-dynamics with data-driven residual learning, resulting in improved performance over state-of-the-art methods in RMSE and R² metrics on six healthy subjects.
Accurately decoding human motion intentions from surface electromyography (sEMG) is essential for myoelectric control and has wide applications in rehabilitation robotics and assistive technologies. However, existing sEMG-based motion estimation methods often rely on subject-specific musculoskeletal (MSK) models that are difficult to calibrate, or purely data-driven models that lack physiological consistency. This paper introduces a novel Physics-Embedded Neural Network (PENN) that combines interpretable MSK forward-dynamics with data-driven residual learning, thereby preserving physiological consistency while achieving accurate motion estimation. The PENN employs a recursive temporal structure to propagate historical estimates and a lightweight convolutional neural network for residual correction, leading to robust and temporally coherent estimations. A two-phase training strategy is designed for PENN. Experimental evaluations on six healthy subjects show that PENN outperforms state-of-the-art baseline methods in both root mean square error (RMSE) and $R^2$ metrics.