title: Several practical issues toward implementing myoelectric pattern recognition for stroke rehabilitation.
abstract: 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.
authors: Institute of Biomedical Engineering, University of Science and Technology of China, Hefei, Anhui, China; Sensory Motor Performance Program, Rehabilitation Institute of Chicago, IL, USA; Department of Physical Medicine & Rehabilitation, Northwestern University, Chicago, IL, USA.
authors: Yun
authors: Y
authors: Li
authors: Institute of Biomedical Engineering, University of Science and Technology of China, Hefei, Anhui, China. Electronic address: xch@ustc.edu.cn.
authors: Xiang
authors: X
authors: Chen
authors: Institute of Biomedical Engineering, University of Science and Technology of China, Hefei, Anhui, China; Sensory Motor Performance Program, Rehabilitation Institute of Chicago, IL, USA.
authors: Xu
authors: X
authors: Zhang
authors: Institute of Biomedical Engineering, University of Science and Technology of China, Hefei, Anhui, China; Sensory Motor Performance Program, Rehabilitation Institute of Chicago, IL, USA; Department of Physical Medicine & Rehabilitation, Northwestern University, Chicago, IL, USA.
authors: Ping
authors: P
authors: Zhou
mesh_terms: D001132:Arm / Q000502:physiology; D002908:Chronic Disease; D004566:Electrodes; D004576:Electromyography / Q000295:instrumentation / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D009068:Movement / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D012815:Signal Processing, Computer-Assisted; D000071939:Stroke Rehabilitation
keywords: Myoelectric control; Pattern recognition; Stroke rehabilitation; Surface electromyography