Abstract
Due to the fact the sEMG can directly reflect the human neuromuscular activity, motion pattern recognition with surface electromyographic signal (sEMG) have been used to achieve human joint motions tracking in the rehabilitation medical engineering filed. However, the sEMG signal is non-Gaussian signal and the non-Gaussian component contains rich information of sEMG signal. Time-domain and frequency-domain features based lower order statistics joint sEMG commonly used cannot characterize the non-Gaussian information of signal. In this paper, we utilized the bispectrum estimators containing non-Gaussian information and the integral of bispectrum slice combined with time-domain features is extracted as features to recognize the elbow motion intention hidden in the filtered sEMG signals from the biceps muscle with good performance compared to other mothods in terms of classification accuracy.
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