期刊
MEDICAL ENGINEERING & PHYSICS
卷 36, 期 6, 页码 754-760出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.medengphy.2014.01.005
关键词
Surface electromyography; Pattern recognition; Myoelectric control; Stroke rehabilitation
资金
- Fundamental Research Funds for the Central Universities of China [WK2100230002]
- National Nature Science Foundation of China (NSFC) [61271138]
- China Scholarships Council (CSC)
- Rehablitation Institute of Chicago
High density surface electromyogram (sEMG) recording and pattern recognition techniques have demonstrated that substantial motor control information can be extracted from neurologically impaired muscles. In this study, a series of pattern recognition parameters were investigated in classification of 20 different movements involving the affected limb of 12 chronic stroke subjects. The experimental results showed that classification performance could be improved with spatial filtering and be maintained with a limited number of electrodes. It was also found that appropriate adjustment of analysis window length, sampling rate, and high-pass cut-off frequency in sEMG conditioning and processing would be potentially useful in reducing computational cost and meanwhile ensuring classification performance. The quantitative analyses are useful for practical myoelectric control toward improved stroke rehabilitation. (C) 2014 IPEM. Published by Elsevier Ltd. All rights reserved.
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