Design of a Regression Model for Four Grasping Patterns and Three Grip Force Intensities of a Myoelectric Prosthetic Hand

Jiho Noh; Woorim Cho; Jae-Hyo Kim

doi:10.7736/KSPE.2018.35.8.809

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저널정보

Korean Society for Precision Engineering
Journal of the Korean Society for Precision Engineering 학술저널
Journal of the Korean Society for Precision Engineering Vol.35 No.8
2018.8 809 - 816 (8page)
DOI : 10.7736/KSPE.2018.35.8.809

저자정보

Jiho Noh (한동대학교)
Woorim Cho (한동대학교)
Jae-Hyo Kim (한동대학교)

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초록·키워드

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Conventional prosthetic hands require users to activate designated muscles or press buttons to select among predefined grasping patterns. These methods are time-consuming and increase muscle fatigue. This study proposes a regression model that differentiates multiple muscle activation patterns allowing the user to select a desired grasping pattern. We classified four hand primitives and three force intensities, which can reflect the intention of prosthetic hand users. An 8-channel band-type sEMG sensor was used to measure myoelectric signals from an amputated upper-arm. To acquire the sEMG data, the amputee was instructed to imagine four hand primitives (fist, open hand, flexion, and extension) with three levels of force intensity (low, medium, and high). Time-domain features (mean average value, variance, waveform length, and root mean square) were extracted from the sEMG signal and classified using a Support Vector Machine. The hand primitives and force intensities had accuracies of 95% and 90%, respectively. Results indicate the regression model reflected the user’s intention to select different grasping patterns, and is thus expected to improve the quality of life of amputees.

#Prosthetic hand (전동 의수) #Grasping (잡기 동작) #Regression model (추정 모델) #Movement primitive (동작 프리미티브) #Electromyographic signals (근전도 신호)