Novel algorithm for real-time onset detection of surface electromyography in step-tracking wrist movements

We present a novel algorithm for real-time detection of the onset of surface electromyography signal in step-tracking wrist movements. The method identifies abrupt increase of the quasi-tension signal calculated from sEMG resulting from the step-by-step recruitment of activated motor units. We assessed the performance of our proposed algorithm using both simulated and real sEMG signals, and compared with two existing detection methods. Evaluation with simulated sEMG showed that the detection accuracy of our method is robust to different signal-to-noise ratios, and that it outperforms the existing methods in terms of bias when the noise is large (low SNR). Evaluation with real sEMG analysis also indicated better detection performance compared to existing methods.