Estimating muscle force based on a neuromuscular decoding approach adaptive to fatigue conditions

Abstract

Electromyography (EMG) has been extensively employed for precise muscle force estimation. Although methods based on motor unit (MU) activities (the smallest functional unit of the neuromuscular control system) have achieved superior performance, their effectiveness in fatiguing conditions remains unknown. This study aims to investigate the performance of existing MU-based muscle force estimation methods in fatiguing conditions, including the representative neuromuscular decoding (NMD) method and firing rate (FR) −based method. Compared to FR-based method, the NMD method specifically accounts for the differences in force contributions among MUs and the force twitch response of muscle fibers. We conducted fatigue induction experiment to collect force and high-density surface EMG data from the abductor pollicis brevis muscles of eight subjects during their performance of thumb abduction. Subsequently, the analysis of myoelectric manifestation of fatigue was performed and four training–testing strategies were designed to examine the effect of fatigue on force estimation methods. Experimental results indicated that the NMD method achieved superior and stable performance (assessed through root mean square difference between estimated and actual force) across four strategies (p >0.05 revealed by ANOVA), with only slight performance decline when fatigue data was involved in training or/and testing phase. Conversely, fatigue caused significant performance degradation when using the FR-based and other five comparative methods (p <0.05). These findings demonstrate that the NMD approach is adaptive to fatiguing MU activities, which can provide precise and robust muscle force estimation against fatigue in motor control and human–machine interfaces.