Study on the sEMG Driven Upper Limb Exoskeleton Rehabilitation Device in Bilateral Rehabilitation
Muye Pang*, Shuxiang Guo**, ***, and Zhibin Song**
*Graduated School of Engineering, Kagawa University, 2217-20 Hayashi-cho, Takamatsu 761-0396, Japan
**Department of Intelligent Mechanical Systems Engineering, Kagawa University, 2217-20 Hayashi-cho, Takamatsu 761-0396, Japan
***College of Automation, Harbin Engineering University, 145 Nantong Street, Harbin, Heilongjiang, China
This study presents an implementation of a continuous upper limb motion recognition method based on surface electromyography (sEMG) into control of an Upper Limb Exoskeleton Rehabilitation Device (ULERD). The raw sEMG hardly can be used directly as a reference control signals due to various influences. A feature extraction method, namely, an autoregressive algorithm, was thus applied to extract features of sEMG. The features of sEMG are usually used as switching signals to indicate whether activation happens. In this study, a continuous recognition is implemented using the coefficients of an AR model because of the coefficients’ characteristics of fitting the trend in sEMG signals. Because of the low signal-to-noise ratio of sEMG, the optimal order of the AR model was calculated based on the Akaike Information Criterion for a good fit to raw signals. Back-propagation neural networks were then trained using coefficients to recognize motion. Recognition results were used as the control source for the rehabilitation device. Experimental results showed that this method is effective for obtaining a control source through raw sEMG signals derived fromthe unaffected arm for motor control of a ULERD equipped on the affected armduring bilateral rehabilitation in real-time.
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