Research on Upper Limb Muscle Fatigue Detection of Power Workers Using FMG

Aiming at the difficult problem of muscle fatigue detection faced by power workers in the complex environment of power grid, this paper proposes a new scheme of upper limb muscle fatigue detection based on FMG signal. The research focus of this scheme includes the following three aspects: First, FMG signal is used as the source of muscle strength data. Compared with traditional methods, FMG signal can better adapt to the complex environment of the power grid, and can reflect the muscle state more accurately, showing higher sensitivity and specificity; Secondly, support vector machine (SVM) classifier is used to identify the muscle strength data efficiently. Finally, genetic algorithm (GA) is used to optimize the penalty parameters C and kernel parameters γ of SVM classifier, and then the GA-SVM model is constructed. The experimental results show that the FMG acquisition system proposed in this scheme can effectively detect muscle pressure changes, and the average classification accuracy of GA-SVM model for muscle pressure signals under fatigue state is more than 97.5%, which significantly improves the accuracy and reliability of muscle fatigue detection, and provides a new technical scheme for the health monitoring of electric power workers.