A New Approach to Noninvasive-Prolonged Fatigue Identification Based on Surface EMG Time-Frequency and Wavelet Features

Abstract

In sports, fatigue management is vital as adequate rest builds strength and enhances performance, whereas inadequate rest exposes the body to prolonged fatigue (PF) or also known as overtraining. This paper presents PF identification and classification based on surface electromyography (EMG) signals. An experiment was performed on twenty participants to investigate the behaviour of surface EMG during the inception of PF. PF symptoms were induced in accord with a five-day Bruce Protocol treadmill test on four lower extremity muscles: the biceps femoris (BF), rectus femoris (RF), vastus medialis (VM), and vastus lateralis (VL). The results demonstrate that the experiment successfully induces soreness, unexplained lethargy, and performance decrement and also indicate that the progression of PF can be observed based on changes in frequency features (ΔFmed and ΔFmean) and time features (ΔRMS and ΔMAV) of surface EMG. This study also demonstrates the ability of wavelet index features in PF identification. Using a naïve Bayes (NB) classifier exhibits the highest accuracy based on time and frequency features with 98% in distinguishing PF on RF, 94% on BF, 9% on VL, and 97% on VM. Thus, this study has positively indicated that surface EMG can be used in identifying the inception of PF. The implication of the findings is significant in sports to prevent a greater risk of PF.