Preliminary Results of EMG Amplitude Estimation with a Muscle Twitch Model

Surface electromyography (sEMG) has been widely used to estimate muscle activity. However, satisfying both low variability and rapid responsiveness of muscle activities using standard signal processing techniques such as the moving average (MAV), root mean square (RMS), and low-pass filter continues to present challenges. To address these issues, we propose a new method for EMG amplitude estimation by using a detection algorithm of motor unit action potentials (MUAPs) and a muscle twitch model. Our method significantly outperforms the standard methods (P<0.01) in estimating muscle activity. This work may prove useful in physiological research, medical treatment, and rehabilitation.