Real-time implementation of a self-recovery EMG pattern recognition interface for artificial arms

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 c...

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Bibliographic Details
Published in2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) Vol. 2013; pp. 5926 - 5929
Main Authors Zhang, Xiaorong, Huang, He, Yang, Qing
Format Conference Proceeding Journal Article
LanguageEnglish
Published United States IEEE 01.01.2013
Subjects
Algorithm design and analysis
Algorithms
Artificial Limbs
Classification algorithms
Electromyography
Electromyography - methods
Equipment Design
Feature extraction
Humans
Pattern recognition
Pattern Recognition, Automated - methods
Prostheses and Implants
Real-time systems
Software
Training
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ISSN1094-687X
1557-170X
DOI10.1109/EMBC.2013.6610901

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Summary: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.
ISSN:1094-687X
1557-170X
DOI:10.1109/EMBC.2013.6610901