NDLI: Identification of finger force and motion from forearm surface electromyography

Content Provider	IEEE Xplore Digital Library
Author	Xiufei Li Jiantin Fu Linfei Xiong Yizhi Shi Davoodi, R. Yao Li
Copyright Year	2015
Description	Author affiliation: Chongqing Inst. of Green & Intell. Technol., Chongqing, China (Xiufei Li; Jiantin Fu; Linfei Xiong; Yizhi Shi; Davoodi, R.; Yao Li)
Abstract	Forearm surface electromyogram (sEMG) is a cumulative signal of the muscle contractions which leads to wrist and finger movement. In this study, we have collected six channels of sEMG from forearm muscles and touch forces at five fingertips simultaneously. Novel classification algorithms were then developed employing Time-Domain Auto-Regression (TD-AR), Principal Component Analysis (PCA), Artificial Neural Network (ANN), and Quadratic Models to predict finger motion and force from the forearm sEMG. The prediction models demonstrate high prediction accuracy and computational efficiency to estimate both the force and motion simultaneously. The models developed in this work provide a new approach for real time force and motion estimation in prosthetic limbs and other man-machine systems.
Starting Page	316
Ending Page	321
File Size	713133
Page Count	6
File Format	PDF
e-ISBN	9781479977727
DOI	10.1109/MFI.2015.7295827
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-09-14
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Thumb Force Predictive models Electromyography Muscles Electrodes
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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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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