Effects of muscle fatigue on the usability of a myoelectric human-computer interface

Electroencephalography Reflects User Satisfaction in Controlling Robot Hand through Electromyographic Signals

This study addresses time intervals during robot control that dominate user satisfaction and factors of robot movement that induce satisfaction. We designed a robot control system using electromyography signals. In each trial, participants were exposed to different experiences as the cutoff frequencies of a low-pass filter were changed. The participants attempted to grab a bottle by controlling a robot. They were asked to evaluate four indicators (stability, imitation, response time, and movement speed) and indicate their satisfaction at the end of each trial by completing a questionnaire. The electroencephalography signals of the participants were recorded while they controlled the robot and responded to the questionnaire. Two independent component clusters in the precuneus and postcentral gyrus were the most sensitive to subjective evaluations. For the moment that dominated satisfaction, we observed that brain activity exhibited significant differences in satisfaction not immediately after feeding an input but during the later stage. The other indicators exhibited independently significant patterns in event-related spectral perturbations. Comparing these indicators in a low-frequency band related to the satisfaction with imitation and movement speed, which had significant differences, revealed that imitation covered significant intervals in satisfaction. This implies that imitation was the most important contributing factor among the four indicators. Our results reveal that regardless of subjective satisfaction, objective performance evaluation might more fully reflect user satisfaction.

Application of Surface Electromyography in Exercise Fatigue: A Review

Exercise fatigue is a common physiological phenomenon in human activities. The occurrence of exercise fatigue can reduce human power output and exercise performance, and increased the risk of sports injuries. As physiological signals that are closely related to human activities, surface electromyography (sEMG) signals have been widely used in exercise fatigue assessment. Great advances have been made in the measurement and interpretation of electromyographic signals recorded on surfaces. It is a practical way to assess exercise fatigue with the use of electromyographic features. With the development of machine learning, the application of sEMG signals in human evaluation has been developed. In this article, we focused on sEMG signal processing, feature extraction, and classification in exercise fatigue. sEMG based multisource information fusion for exercise fatigue was also introduced. Finally, the development trend of exercise fatigue detection is prospected.

Weighted-Cumulated S-EMG Muscle Fatigue Estimator

This paper addresses a new approach to objectively evaluate muscle fatigue in isometric and dynamic physical exertions using surface electromyography (S-EMG). The emphasis of this proposal is to preserve the spectral signature of the muscle fatigue phenomenon while reducing the spatial effects of electrode localization, and decreasing the disparity of results obtained by the same experimental protocol at different times. A cumulated and normalized modeling was sought to make evident the nonstationary characteristics of muscle fatigue that is gradually identified with its inertia and intensity. A metric involving the proposal of temporal, frequency, and time-frequency weighted-cumulated indicators is presented. Results based on real signals are shown for isometric and dynamic experimental protocols. Performance comparison of the various proposed weighted-cumulated indexes is shown and discussed. The presented approach for the objective cumulative evaluation of muscle fatigue with S-EMG signals has shown to be promising.