Novel slope-based onset detection algorithm for electromyographical signals

Abstract

Electromyography (EMG) is a technique of acquiring neuromuscular activity of muscle. Onset and offset gives information about activation and deactivation timings of motor units. This paper proposes a novel slope-based algorithm for onset and offset detection. EMG data are collected from different muscle of different subjects using surface EMG electrodes. Data is divided into smaller windows and average instantaneous amplitude (AIA) and slope is calculated for each window. A threshold is decided to avoid baseline noise. Below threshold, maximum and minimum slope is detected as the onset and offset respectively. The results are accurate compared to single threshold and double threshold method. Accuracy increases with computation complexity (arithmetic calculations); if compared with root mean square (RMS)-based algorithm. The only limitation is decrease in accuracy if signal is acquired between two muscle contractions. The proposed slope-based onset detection algorithm can be way out between accuracy and computational complexity.