Robust decoding of hand movement for mitigating muscle contraction variability from surface EMG signals.

Abstract

The widespread application of surface electromyography (sEMG) pattern recognition in artificial limbs largely relies on a robust decoding system that continuously and accurately identifies user hand movement intention. However, the muscle contraction variability significantly distorts the time-frequency components in sEMG signals, thereby weakening the performance of hand movement decoding. This paper aims to propose a novel framework to achieve robust decoding of hand movement for mitigating muscle contraction variability from sEMG signals. We collect two-channel sEMG signals from eight healthy subjects performing six hand movements under three muscle contraction levels (normal level, medium level, high level). Fifteen popular sEMG features are extracted, and five classic classifiers are employed for decoding hand movements. We analyze how muscle contraction variability degrades hand movement decoding performance and propose a simulation-driven feature mapping framework. It maps abnormal sEMG features from medium and high levels back to normal levels by Feature Denoising Network using simulated sEMG features. Without the proposed framework, decoding accuracy drops markedly when muscle contraction intensity is switched from normal level to medium and high levels, resulting in accuracy losses of at least 32.32% for observed muscle contraction levels and 37.50% for predicted muscle contraction levels. With the proposed framework, decoding accuracy is significantly improved, with accuracy improvements of at least 31.82% for observed muscle contraction levels and 26.99% for predicted muscle contraction levels. The paper provides a feasible solution to challenge of muscle contraction variability for robust hand movement decoding from sEMG signals, which holds potential reference value in the development of myoelectric hand prosthesis.