An Adaptive Two-Step Method for Voluntary Muscle Activity Detection Using sEMG Signals with False Background Spikes

Accurate detection of muscle activity is crucial for rehabilitation systems that rely on voluntary control. However, the presence of false background spikes interference can significantly impede the precise decoding of motion intention. To address this problem, an adaptive two-step method is proposed in this paper for accurately extracting the muscle activation intervals from sEMG signals. In the first step, an adaptive threshold is used to identify potential onsets and offsets of the envelope. Then, a combination of an evaluation equation and the k-means clustering technique is utilized to eliminate incorrect onsets and offsets. In the second step, two peak points closing proximity to the onset and offset are identified. The tangency of the envelope's onset and offset with the respective peak points is then determined, with the intersection point of these tangencies is considered as the final onset and offset. The proposed method is tested on semi-synthetic sEMG signals and real sEMG signals, and compared with state-of-the-art algorithms. The results clearly indicate that the proposed method produces the best detection performance, and eliminates the requirement for parameter selection, greatly facilitating the signal extraction process.