Research on fatigue characteristics of static muscle contraction based on surface electromyography

Abstract

Human beings serve as a crucial resource and labor force in daily life, industrial, and agricultural production. To complete specific tasks, certain muscle groups must maintain a certain level of contraction during manual labor. Sustaining the same posture for a long time can cause muscle fatigue and chronic strain diseases. Quantifying fatigue effectively and predicting its intensity can help address these issues. Therefore, this paper investigates the development of a quantitative evaluation and prediction method for muscle fatigue intensity during isometric contractions. It focuses on seven standard upper limb work postures, leveraging surface electromyography (sEMG) collected from these postures. The study extracts and selects time-domain and frequency-domain features to identify characteristic parameters that effectively represent the fatigue state. Based on these parameters, a back propagation (BP) neural network-based muscle fatigue intensity classification model is established, enabling the categorization and quantitative assessment of muscle fatigue levels. Considering the temporal nature of fatigue progression, a nonlinear autoregressive dynamic neural network model is constructed for predicting fatigue characteristics. Bayesian regularization is employed as the training algorithm following comparative selection. By integrating the fatigue classification model with the feature prediction model, the study enables the forecast of muscle fatigue severity at future time points, achieving an accuracy rate of up to 95%.