Assessment of Human Muscle Fatigue from Surface EMG Signals Recorded during Isometric Voluntary Contractions

Abstract

Human muscle fatigue involves both a decrease in the frequency and an increase in the amplitude of a surface electromyographic (EMG) signal. To determine muscle fatigue indices, we break a surface EMG signal into 32-subbands by using a cosine modulated filter bank. The surface EMG signals analyzed during this research were recorded during isometric voluntary contractions. Both the instantaneous mean frequency (IMF) and the instantaneous amplitude (IA) are estimated from the 32-subbands of the filter bank and are used as muscle fatigue indicators. To evaluate the IMF and the IA estimated from the filter bank, two other standard techniques, the spectrogram and the smoothed pseudo Wigner-Ville (SPWV) distribution, were also implemented. A regression-free area ratio was adopted to compute an EMG index from both estimates the IMF and IA. These indices were then classified — by using a joint analysis of frequency and amplitude (JASA) — into one of the four muscle activity regions: muscle increase force, muscle recovery, muscle decrease force, and muscle fatigue. We found that EMG indices derived from the proposed filter bank are equivalent to those EMG indices derived from the spectrogram and the SPWV distribution. Moreover, muscle fatigue indices derived from the filter bank indicated that they could be used as indices to determine human muscle fatigue.