A surface EMG signals-based real-time continuous recognition for the upper limb multi-motion

This paper was aimed at the continuous recognition of the upper limb multi-motion during the upper limb movement for rehabilitation training. The amplitude of the surface electromyographic (sEMG) signals change during movement of the upper limb and the features of sEMG signals are different with the changes. These variances in the features represent the different statuses of the upper limb. Recognizing the variances will lead to recognition of the upper limb motion. In this study, sEMG signals were recorded through five noninvasive electrodes attached on the anatomy points of the upper limb and an autoregressive model was used to extract the features of the detected sEMG signals. After that the Back-propagation Neural Networks was applied to recognize the patterns of the upper arm motion using the variant features as the training and input data. Three volunteers participated in the real-time experiment and the results stated that this method is effective for a real-time continuous recognition of the upper limb multi-motions.