Fatigue-Sensitivity Comparison of sEMG and A-Mode Ultrasound based Hand Gesture Recognition.

Abstract

Though physiological signal based human-machine interfaces (HMIs) have recently developed rapidly, their practical use is restricted by many real-world environmental factors, one of which is muscle fatigue. This paper explores the sensitivities between surface electromyography (sEMG) and A-mode ultrasound (AUS) sensing modalities subject to muscle fatigue in the context of hand gesture recognition tasks. Two metrics, mean classification accuracy ( mCA) and decline rate ( DR), are proposed to evaluate the accuracy and muscle fatigue sensitivity between sEMG and AUS based HMIs. Muscle fatigue inducing experiment was designed and eight subjects were recruited to participate in the experiment. The gesture recognition accuracies of sEMG and AUS under non-fatigue state and fatigue state are compared through Mahalanobis distance based classifier linear discriminant analysis (LDA). In addition, Mahalanobis distance based metrics, repeatability index ( RI) and separability index ( SI), are introduced to evaluate the changes in the feature distribution during muscle fatigue and reveal the cause of the fatigue sensitivity difference between sEMG and AUS signals. The experimental results demonstrate that the fatigue robustness of AUS signal is better than that of sEMG signal. Specifically, with the employment of the LDA classifier trained under non-fatigue state, the testing accuracy of the sEMG signal on the non-fatigue state is 94.96%, while reduce to 68.26% on the fatigue state. The testing accuracy of the AUS signal on the corresponding states is 99.68% and 91.24% respectively. AUS signal attains higher mCA and lower DR, indicating that it has advantages over sEMG signal in terms of both accuracy and muscle fatigue sensitivity. In addition, the RI and RI/SI analysis reveal that before and after muscle fatigue, the consistency of AUS feature distribution is better than that of sEMG. These research outcomes validate that AUS is more tolerant to feature migration caused by muscle fatigue than sEMG.