Wavelet Analysis of Surface Electromyography to Determine Muscle Fatigue

Abstract

Muscle fatigue is often a result of unhealthy work practice. It has been known for some time that there is a significant change in the spectrum of the electromyography (EMG) of the muscle when it is fatigued. Due to the very complex nature of this signal however, it has been difficult to use this information to reliably automate the process of fatigue onset determination. If such a process implementation were feasible, it could be used as an indicator to reduce the chances of work-place injury. This research report on the effectiveness of the wavelet transform applied to the EMG signal as a means of identifying muscle fatigue. We report that with the appropriate choice of wavelet functions and scaling factors, it is possible to achieve reliable discrimination of the fatigue phenomenon, appropriate to an automated fatigue identification system.