A new approach to preprocessing of EMG signal to assess the correctness of muscle condition

Abstract

Purpose: Electromyography (EMG) is a technique dealing with the recording and analysis of myoelectric signals formed by physiological variations in the muscle fiber membrane. The voltage potential of surface myoelectrical signals (sEMG) varies over time and depends on the characteristics of the individual subject. One of the main drawbacks of sEMG analysis is that the given acquisition conditions strongly determine the amplitude of the signal. The analysis of sEMG requires appropriate preprocessing, including proper filtering and artifact removal. Moreover, the sEMG data must be converted to a scale standardized for all measurements. This research aimed both to propose a method of sEMG processing to eliminate the occurring disturbances, in particular impulsive artifacts, and to determine the level of muscle excitation based on normalized sEMG. The analysis of muscle excitation level can be applied to assess muscle activity during physical activity. Design/methodology/approach: The proposed algorithm uses set of digital filters, probabilistic distribution and the decomposition of the sEMG signal to attenuate artifacts. Variance analysis of the sEMG derivative is used to determine muscle excitation. The sEMG signals were acquired with the VICON system with the sampling frequency set at 1000 Hz, and processed in MATLAB. During sEMG recordings, standard silver/silver chloride (Ag/AgCl) surface electrodes were used. Findings: The suggested technique was validated using sEMG recorded for eight persons during deep squat. Normalized excitation was determined for the left and right muscles, the rectus femoris, the vastus medialis, and the biceps femoris. Obtained outcomes indicate a possibility to assess the correctness of muscles condition. Originality/value: The combination of the proposed filter and the analysis variance-based thresholding method can effectively eliminate impulse artifacts within the surface myoelectrical signals. Keywords: Spikes disturbance, EMG, Muscle excitation, Filtering, Kurtosis. Category of the paper: Research paper.