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Top1. Introduction
Muscle fatigue is a common physiological phenomenon in daily sport exercises (Ghamkhar & Kahlaee 2019). The most intuitive feeling of muscle fatigue is muscle weakness or soreness. In physiology, muscle fatigue is the internal reason why the body cannot maintain the expected strength due to the temporary decline of the work or contraction ability of the muscle movement system (Oleksy et al. 2018). As for the mechanism of muscle fatigue, the central fatigue theory believes that fatigue is the result of protective inhibition of the cerebral cortex (McMorris et al. 2018). When a person feels tired, the active protection is taken to avoid body damage. From the perspective of the change of the content of chemical substances in cells, the occurrence of Ca+ movement disturbance, the accumulation of phosphate and other metabolites, the decrease of ATP etc. lead to the change of action conduction potential and the decrease of muscle fiber contraction strength, which causes the subjective feeling of powerlessness (Kanehisa 2019).
When muscle fatigue occurs, it will cause the changes of electromyography (EMG) signals to reflect the body state of the muscle (Jebelli & Lee 2019; Hussain 2019). Through detecting the changes of EMG signals, it can provide early warning before fatigue to prevent further muscle damage, maintain the maximum activity of muscles, and enhance the protection of body. In daily sports, the fatigue caused by overload of muscles may induce permanent damage or injury (Wirth et al. 2021). The early fatigue analysis and evaluation has important role in sports injury recovery and auxiliary training. Therefore, an automatic system that can detect the occurrence of muscle fatigue is particularly useful in sports related scenes. The system can guide users to carry out conscious training and act as a warning device of early fatigue to avoid excessive muscle fatigue and prevent body injury.
The research shows that the contraction mode of muscle can be divided into isometric contraction and isometric contraction (Ato et al. 2019). When the static constant force is applied, it is isometric contraction. When the muscle is moving toward the belly, it is isotonic contraction. Both of these movements can lead to muscle fatigue. Under static contraction, when muscles tend to fatigue, the spectrum of EMG signals will shift to the left and the time domain amplitude will increase. However, due to the limitations of instruments, these studies have been in the exploratory stage. With the development of signal acquisition equipment, more attention has been paid to the research on muscle fatigue. When muscle fatigue occurs, the amplitude and spectral characteristics of EMG signals will change, which has become a consensus in the research.
Since the discovery of muscle fatigue, many researchers have taken a variety of methods to study and evaluate it. In terms of acquisition methods, there are mainly intrusive methods and non-intrusive methods (Nsugbe 2021). Invasive method can collect more accurate physiological signals, but may cause wound damage. It is not widely used in the study of muscle fatigue (Greco et al. 2019). The non-invasive method is easy to obtain through human body surface. Under the premise of ensuring the signal quality, non-invasive method is widely used in muscle analysis (Toro et al. 2019). At present, the mainstream non-invasive research methods include mechanical mapping (MMG) method based on muscle vibration (Cè et al. 2013) and electromyography (EMG) method based on motor unit discharge (Toro et al. 2019). From the research trend, EMG method is more suitable for clinical use. In addition, some studies have shown that MMG signal is the mechanical equivalent signal of surface electromyography (sEMG), which originates from the low-frequency vibration of muscle fibers during contraction and extension (Cifrek et al. 2009). The ultrasonic and infrared imaging methods cannot achieve promising accuracy of sEMG. Thus, this paper adopts sEMG to monitor the muscle fatigue during sports.