Recognition of Muscular Activity in Complex Noise-Contaminated Myoelectric Signals: An Adaptive Two-Step Approach

Surface electromyography (sEMG) signals demonstrate how the muscles react to the control strategies of the nervous system. For stroke patients, the detection of muscle activation is crucial because it allows researchers to better understand their brain control mechanisms. However, extracting the muscular activity precisely from the sEMG signals with various complex noises is a challenge in biomedical data processing. This study presented an adaptive two-step (AdaTS) muscular activity recognition algorithm to handle this problem. The sEMG signal was first pre-extracted using the adaptive threshold approach and annexation method to identify the interval comprising active information. Then, after amplifying the difference of activity and inactivity of the interval signal through the Teager–Kaiser energy operator, the onsets and offsets were determined based on the overall change of the interval signal. The proposed algorithm was tested in semi-synthetic signals, real signals from a public database, and experimentally recorded signals. Compared with other approaches, our method can effectively handle various types of interference and produce the best detection performance. Additionally, the steps of parameter selection and adjustment are removed, which greatly simplifies the practical application.