An Automatic Muscle Activation Detection Using Discrete Wavelet and Integrated Profile: A Comparative Study

Abstract

The surface electromyogram (SEMG) is an electrophysiology signal that can be used in many fields such as biomechanical engineering, medicine and sport. It’s applied to analyze and study the human movement. The SEMG recordings can be contaminated by spurious background spikes, quiescent baseline. These artifacts and noises produce false muscle activation detection. The muscle activation detection during movement depends on several parameters such as the beginning and the end of an activity, the nerve conduction velocity, the on-off interval, etc. In this paper, we conduct a study to detect the activation interval from the biceps brachial muscle using discrete wavelet transform (DWT) for SEMG signal denoising based on thresholding method. We compare our method with the integrated profile method. The results show that our method can effectively reduce the detection error.