Real-time implementation of a self-recovery EMG pattern recognition interface for artificial arms
Document Type
Conference Proceeding
Date of Original Version
10-31-2013
Abstract
EMG pattern classification has been widely studied for decoding user intent for intuitive prosthesis control. However, EMG signals can be easily contaminated by noise and disturbances, which may degrade the classification performance. This study aims to design a real-time self-recovery EMG pattern classification interface to provide reliable user intent recognition for multifunctional prosthetic arm control. A novel self-recovery module consisting of multiple sensor fault detectors and a fast LDA classifier retraining strategy has been developed to immediately recover the classification performance from signal disturbances. The self-recovery EMG pattern recognition (PR) system has been implemented on an embedded system as a working prototype. Experimental evaluation has been performed on an able-bodied subject in real-time to classify three arm movements while signal disturbances were manually introduced. The results of this study may propel the clinical use of EMG PR for multifunctional prosthetic arm control. © 2013 IEEE.
Publication Title, e.g., Journal
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Citation/Publisher Attribution
Zhang, Xiaorong, He Huang, and Qing Yang. "Real-time implementation of a self-recovery EMG pattern recognition interface for artificial arms." Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS (2013): 5926-5929. doi: 10.1109/EMBC.2013.6610901.