A Threshold-Based Approach for Muscle Contraction Detection From Surface EMG Signals

Surface electromyographic (SEMG) signals are commonly used as control signals in prosthetic and orthotic devices. Superficial electrodes are placed on the skin of the subject to acquire its muscular activity through this signal. The muscle contraction episode is then in charge of activating and deactivating these devices. Nevertheless, there is no "gold standard" to detect muscle contraction, leading to delayed responses and false and missed detections. This fact motivated us to propose a new approach that compares a smoothed version of the SEMG signal with a fixed threshold, in order to detect muscle contraction episodes. After preprocessing the SEMG signal, the smoothed version is obtained using a moving average filter, where three different window lengths have been evaluated. The detector was tuned by maximizing sensitivity and specificity and evaluated using SEMG signals obtained from the anterior tibial and gastrocnemius muscles, taken during the walking of five subjects. Our best detector shows values of sensitivity of 87.91%, specificity of 87.15%, and detection delay of 23.75 ms. Future work is directed to the inclusion of a temporal threshold (a double-threshold approach) to minimize false detections and reduce detection delays.