Detection of active segments of lower limbs EMG signal based on time-domain variance combined with short-term energy

Abstract Using EMG signal to realize gesture recognition is a technical difficulty in the application of EMG signal. In order to solve this problem and ensure the accuracy and timeliness of action classification and recognition, it is proposed to use time-domain variance combined with short-term energy to set a double threshold algorithm to detect effective activity segments. Based on the short-term energy and the zero-crossing rate, the minimum energy threshold and the frame window data variance value are designed. Then the original signal is processed by frame length and frameshift, and the complete signal data is divided into several small Windows. Finally, the effective activity segment is intercepted after comparing the upper and lower limits of the time-domain feature extraction variance with the set threshold. The experimental results show that the algorithm is effective in realizing the detection and interception of the start and end points of actions when processing single-channel or multi-channel EMG signals.