High-Density EMG Grip Force Estimation During Muscle Fatigue via Domain Adaptation.

Myoelectric interfaces hold promise for enabling intuitive and natural control of prostheses and exoskeletons. Muscle fatigue, whether due to prolonged use or heavy weight loads, can alter the distribution of electromyographic (EMG) signals, leading to inconsistencies compared to non-fatigued conditions. This presents significant challenges for accurately decoding user intentions. We thus propose a novel estimation method based on domain adaptation to improve grip force estimation accuracy during muscle fatigue. Specifically, the proposed method integrates an adversarial training strategy and an end-to-end deep learning model to align EMG feature distributions across non-fatigue and fatigue states. A baseline model, whose structure was identical to the force estimation network of the proposed method, was used for comparison. Eight subjects performed non-fatigue and fatigue gripping tasks, and grip force estimations were compared with dynamometer gold standard measurements. Results demonstrate that root mean square error (RMSE) of the proposed model was 51.9% lower than that of the baseline model during muscle fatigue. The proposed method leverages labeled data in the non-fatigue domain and employs adversarial objectives to ensure the learned features are applicable to both domains, which eliminates the need to pause to collect force label data in the fatigue domain, expediting and simplifying the calibration process. This study has the potential to improve the ability of myoelectric interfaces during muscle fatigue to enable users to intuitively retrieve and grip objects over extended periods, ultimately improving independence and quality of life.