期刊
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
卷 56, 期 5, 页码 1407-1414出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TBME.2008.2008171
关键词
Amputee; electromyography (EMG); myoelectric; myoelectric signal (MES); pattern recognition; principal components analysis; prostheses; tranrsradial
资金
- Natural Sciences and Engineering Research Council of Canada (NSERC) [171368-03, 217354-01]
- New Brunswick Foundation
- Atlantic Innovation Fund
Information extracted from multiple channels of the surface myoelectric signal (MES) recording sites can be used as inputs to control systems for powered upper limb prostheses. For small, closely spaced muscles, such as the muscles in the forearm, the detected MES often contains contributions from more than one muscle, the contribution from each specific muscle being modified by the dispersive propagation through the volume conductor between the muscle and the detection points. In this paper, the measured raw MES signals are rotated by class-specific principal component matrices to spatially decorrelate the measured data prior to feature extraction. This tunes the data to allow a pattern recognition classifier to better discriminate the test motions. This processing technique was used to significantly (p < 0.01) reduce pattern recognition classification error for both intact. limbed and transradial amputee subjects.
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