Complex Network Properties Analysis of Muscle Fatigue Based on sEMG Signals

Abstract

Fatigue assessment is especially important in long-term physical work and training to avoid injury caused by muscle fatigue. The surface electromyography (sEMG) signal has been widely used to detect muscle fatigue states. The purpose of this article is to establish muscle functional networks and provide a comprehensive assessment of muscle fatigue using multi-channel sEMG signals from the back muscles. A muscle functional network is constructed based on the Pearson correlation coefficients between the channels of sEMG signals, and we explore how the economic properties and small-world properties of the muscle network change with cost. Then, the network threshold is determined by changes in small-world properties. We extract network parameters through complex network methods to quantify and analyze the differences in muscle functional networks under different fatigue states. The most significant results of this novel approach indicate that the economic and small-world properties of the muscle network gradually decrease with increasing fatigue, and network parameters decrease significantly after fatigue. The method presented in this article provides a new foundation for assessing muscle fatigue.