A Proposal for Wearable Controller Device and Finger Gesture Recognition using Surface Electromyography

Ayumu Tsuboi; Mamoru Hirota; Junki Sato; Masayuki Yokoyama; Masao Yanagisawa

doi:10.1145/3145690.3145731

A Proposal for Wearable Controller Device and Finger Gesture Recognition using Surface Electromyography

Ayumu Tsuboi, Mamoru Hirota, Junki Sato, Masayuki Yokoyama, Masao Yanagisawa

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

8 Citations (Scopus)

Abstract

Hand and finger motion is very complicated and achieved by intertwining forearm part (extrinsic) and finger part (intrinsic) muscles. We created a wearable finger-less glove controller using dry electrodes of sEMG(surface Electromyography) and only intrinsic hand muscles were sensed. Our wearable interface device is easy to wear and light-weighted. In offline analysis, we identified the tapping motion of fingers using the wearable glove. Totally eleven features were extracted, and linear discriminant analysis (LDA) was used as a classifier. The average of the discrimination result of in-tersubject analysis was 88.61±3.61%. In online analysis, we created a demo that reflects actual movement in the virtual space by Unity. Our demo showed a prediction of finger motions, and realized the motions in the virtual space.

Original language	English
Title of host publication	SIGGRAPH Asia 2017 Posters, SA 2017
Publisher	Association for Computing Machinery, Inc
ISBN (Electronic)	9781450354059
DOIs	https://doi.org/10.1145/3145690.3145731
Publication status	Published - 2017 Nov 27
Event	SIGGRAPH Asia 2017 Posters, SA 2017 - Bangkok, Thailand Duration: 2017 Nov 27 → 2017 Nov 30

Other

Other	SIGGRAPH Asia 2017 Posters, SA 2017
Country/Territory	Thailand
City	Bangkok
Period	17/11/27 → 17/11/30

Keywords

Finger Gesture Recognition?I/F
Glove Controller
SEMG?Dry Electrode

ASJC Scopus subject areas

Computer Vision and Pattern Recognition
Computer Graphics and Computer-Aided Design

Access to Document

10.1145/3145690.3145731

Cite this

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title = "A Proposal for Wearable Controller Device and Finger Gesture Recognition using Surface Electromyography",

abstract = "Hand and finger motion is very complicated and achieved by intertwining forearm part (extrinsic) and finger part (intrinsic) muscles. We created a wearable finger-less glove controller using dry electrodes of sEMG(surface Electromyography) and only intrinsic hand muscles were sensed. Our wearable interface device is easy to wear and light-weighted. In offline analysis, we identified the tapping motion of fingers using the wearable glove. Totally eleven features were extracted, and linear discriminant analysis (LDA) was used as a classifier. The average of the discrimination result of in-tersubject analysis was 88.61±3.61%. In online analysis, we created a demo that reflects actual movement in the virtual space by Unity. Our demo showed a prediction of finger motions, and realized the motions in the virtual space.",

keywords = "Finger Gesture Recognition?I/F, Glove Controller, SEMG?Dry Electrode",

author = "Ayumu Tsuboi and Mamoru Hirota and Junki Sato and Masayuki Yokoyama and Masao Yanagisawa",

year = "2017",

month = nov,

day = "27",

doi = "10.1145/3145690.3145731",

language = "English",

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note = "SIGGRAPH Asia 2017 Posters, SA 2017 ; Conference date: 27-11-2017 Through 30-11-2017",

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T1 - A Proposal for Wearable Controller Device and Finger Gesture Recognition using Surface Electromyography

AU - Tsuboi, Ayumu

AU - Hirota, Mamoru

AU - Sato, Junki

AU - Yokoyama, Masayuki

AU - Yanagisawa, Masao

PY - 2017/11/27

Y1 - 2017/11/27

N2 - Hand and finger motion is very complicated and achieved by intertwining forearm part (extrinsic) and finger part (intrinsic) muscles. We created a wearable finger-less glove controller using dry electrodes of sEMG(surface Electromyography) and only intrinsic hand muscles were sensed. Our wearable interface device is easy to wear and light-weighted. In offline analysis, we identified the tapping motion of fingers using the wearable glove. Totally eleven features were extracted, and linear discriminant analysis (LDA) was used as a classifier. The average of the discrimination result of in-tersubject analysis was 88.61±3.61%. In online analysis, we created a demo that reflects actual movement in the virtual space by Unity. Our demo showed a prediction of finger motions, and realized the motions in the virtual space.

AB - Hand and finger motion is very complicated and achieved by intertwining forearm part (extrinsic) and finger part (intrinsic) muscles. We created a wearable finger-less glove controller using dry electrodes of sEMG(surface Electromyography) and only intrinsic hand muscles were sensed. Our wearable interface device is easy to wear and light-weighted. In offline analysis, we identified the tapping motion of fingers using the wearable glove. Totally eleven features were extracted, and linear discriminant analysis (LDA) was used as a classifier. The average of the discrimination result of in-tersubject analysis was 88.61±3.61%. In online analysis, we created a demo that reflects actual movement in the virtual space by Unity. Our demo showed a prediction of finger motions, and realized the motions in the virtual space.

KW - Finger Gesture Recognition?I/F

KW - Glove Controller

KW - SEMG?Dry Electrode

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M3 - Conference contribution

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A Proposal for Wearable Controller Device and Finger Gesture Recognition using Surface Electromyography

Abstract

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Keywords

ASJC Scopus subject areas

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