Recognition of Motion of Human Upper Limb using SEMG in Real Time: Towards Bilateral Rehabilitation

The surface electromyographic (sEMG) signal has been researched in many fields, such as medical diagnoses and prostheses control. In this paper, recognition of motion of human upper limb by processing sEMG signal in real time was proposed for application in bilateral rehabilitation, in which hemiplegia patients trained their impaired limbs by rehabilitation device based on motion of the intact limbs. In the processing of feature exaction of sEMG, Wavelet packet transform (WPT) and autoregressive (AR) model were used. The effect of feature exaction with both methods was discussed through the processing of classification where Back-propagation Neural Networks were trained. The experimental results show both methods can obtain reliable accuracy of motion pattern recognition. Moreover, on the experimental condition, the recognized accuracy of WPT is higher than that of AR model.