Study on the sEMG Driven Upper Limb Exoskeleton Rehabilitation Device in Bilateral Rehabilitation

Abstract

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.

Study on the comparison of three different upper limb motion recognition methods

“…With the development of Electromyography technology, this biological signal detected from contracting muscle has been widely used in biorobots control [1]- [3], body motion recognition [4]- [5], rehabilitation [6] and athlete training. The essential characteristic of EMG signal makes it very convenient and direct to represent status of skeletal muscles.…”

Posture recognition of elbow flexion and extension using sEMG signal based on multi-scale entropy

“…In this paper, continuous motion recognition in human machine interaction and bilateral rehabilitation was implemented based on sEMG signal [19], [20] in which only one channel signal processing was concentrated on, that is only one active muscle is recruited in our search. [21]showed EMG-driven state space model could perform well to estimate continuous joint angular displacement and velocity in elbow flexion/ extension.…”

Finger joint continuous interpretation based on sEMG signals and muscular model

“…After its discovery, EMG signal has been widely used in biorobots [3]- [6] control and some other fields such as rehabilitation [7], human body motion detection [8] and athlete training. The nature feature of the EMG signal, which directly represents the activation potentials of skeleton muscle, makes it very convenient and direct in representing the status of muscles.…”