NDLI: Real-time isometric pinch force prediction from sEMG

Content Provider	IEEE Xplore Digital Library
Author	Changmok Choi Suncheol Kwon Wonil Park Mihye Shin Jung Kim
Copyright Year	2008
Description	Author affiliation: Dept. of Mech. Eng., KAIST, Seoul (Changmok Choi; Suncheol Kwon; Wonil Park; Mihye Shin; Jung Kim)
Abstract	This paper describes a real-time isometric pinch force prediction algorithm using surface electromyogram (sEMG). The activities of seven muscles related to the movements of the thumb and index finger joints, which are observable using surface electrodes, were recorded during pinch force experiments. For the successful implementation of the real-time prediction algorithm, an off-line analysis was performed using the recorded activities. From the seven muscles, four muscles were selected for monitoring using the Fisher linear discriminant paradigm in an off-line analysis, and the recordings from these four muscles provided the most effective information for mapping sEMG to the pinch force. An ANN structure was designed to perform efficient training and to avoid both under-fitting and over-fitting problems. Finally, the pinch force prediction algorithm was tested with five volunteers and the results were evaluated using two criteria: normalized root mean squared error (NRMSE) and correlation (CORR). The training time for the subjects was only 2 min 29 sec, but the prediction results were successful with NRMSE = 0.093 plusmn0.047 and CORR = 0.957 plusmn0.031. These results imply that the proposed algorithm is useful to measure the generated pinch force without force sensors. The possible applications of the proposed method include controlling bionic finger robot systems to overcome finger paralysis or amputation.
Starting Page	79
Ending Page	84
File Size	984833
Page Count	6
File Format	PDF
ISBN	9781424428823
DOI	10.1109/BIOROB.2008.4762823
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-10-19
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Electrodes Algorithm design and analysis Force measurement Fingers Thumb Muscles Prediction algorithms Performance analysis Force sensors Monitoring
Content Type	Text
Resource Type	Article

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