NDLI: Conditioning and Sampling Issues of EMG Signals in Motion Recognition of Multifunctional Myoelectric Prostheses

Content Provider	Springer Nature Link
Author	Li, Guanglin Li, Yaonan Yu, Long Geng, Yanjuan
Copyright Year	2011
Abstract	Historically, the investigations of electromyography (EMG) pattern recognition-based classification of intentional movements for control of multifunctional prostheses have adopted the filter cut-off frequency and sampling rate that are commonly used in EMG research fields. In practical implementation of a multifunctional prosthesis control, it is desired to have a higher high-pass cut-off frequency to reduce more motion artifacts and to use a lower sampling rate to save the data processing time and memory of the prosthesis controller. However, it remains unclear whether a high high-pass cut-off frequency and a low-sampling rate still preserve sufficient neural control information for accurate classification of movements. In this study, we investigated the effects of high-pass cut-off frequency and sampling rate on accuracy in identifying 11 classes of arm and hand movements in both able-bodied subjects and arm amputees. Compared to a 5-Hz high-pass cut-off frequency, excluding the EMG components below 60 Hz decreased the average accuracy of 0.1% in classifying the 11 movements across able-bodied subjects and increased the average accuracy of 0.1 and 0.4% among the transradial (TR) and shoulder disarticulation (SD) amputees, respectively. Using a 500 Hz instead of a 1-kHz sampling rate, the average classification accuracy only dropped about 2.0% in arm amputees. The combination of sampling rate and high-pass cut-off frequency of 500 and 60 Hz only resulted in about 2.3% decrease in average accuracy for TR amputees and 0.4% decrease for SD amputees in comparison to the generally used values of 1 kHz and 5 Hz. These results suggest that the combination of sampling rate of 500 Hz and high-pass cut-off frequency of 60 Hz should be an optimal selection in EMG recordings for recognition of different arm movements without sacrificing too much of classification accuracy which can also remove most of motion artifacts and power-line interferences for improving the performance of myoelectric prosthesis control.
Starting Page	1779
Ending Page	1787
Page Count	9
File Format	PDF
ISSN	00906964
Journal	Annals of Biomedical Engineering
Volume Number	39
Issue Number	6
e-ISSN	15739686
Language	English
Publisher	Springer US
Publisher Date	2011-02-04
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Electromyography Multifunctional myoelectric prosthesis Signal conditioning and sampling Limb amputation Pattern recognition Biophysics and Biological Physics Mechanics Biochemistry Biomedical Engineering Biomedicine general
Content Type	Text
Resource Type	Article
Subject	Biomedical Engineering

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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