Study on recognition of upper limb motion pattern using surface EMG signals for bilateral rehabilitation

Abstract

Surface electromyographic signal (sEMG) is deep related with the activation of motor muscle and motion of human body, which can be used to estimate the intention of the human movement. So it is advantaged in the application of bilateral rehabilitation, where hemiplegic patients can perform rehabilitation training to their impaired limbs following the motion of intact limbs by using a certain training tool. In this paper, we discussed the motion pattern recognition of human upper limb based on the sEMG signals. The main features of motion patterns based on sEMG signals are extracted via wavelet packet transform. Because the sEMG signal is a kind of non-stationary signal and there are many factors which can affect it like inherent noise, cross talk and so on. Therefore, a simple new method to obtain the trend of sEMG with weighted peaks as features was proposed and support vector machine (SVM) is utilized as the classifier. The contrastive experimental results show that the proposed method improved the recognition rate.