Gesture recognition system using optical muscle deformation sensors

Satoshi Hosono, Shoji Nishimura, Ken Iwasaki, Emi Tamaki

研究成果: Conference contribution

抄録

Due to the spread of VR(Virtual Reality)/AR(Augmented Reality) applications, gesture input method will be required. In this research, a gesture recognition system is suggested using the optical muscle deformation sensors. Our gesture recognition system adapts machine learning with 8 channel optical muscle deformation sensors on the forearm which doesn’t disturb the movement of the hand. In our experiment, significant differences were found in t-test. It was found that SVM can recognize gesture with higher accuracy more than Logistic Regression. In addition, we conducted an experiment to distinguish the state of bending each finger joint. As a result, it was found that the open hand gesture is erroneously recognized as PIP bent gesture.

本文言語English
ホスト出版物のタイトルProceedings of the 2nd International Conference on Electronics, Communications and Control Engineering, ICECC 2019
出版社Association for Computing Machinery
ページ12-15
ページ数4
ISBN(電子版)9781450362634
DOI
出版ステータスPublished - 2019 4 13
イベント2nd International Conference on Electronics, Communications and Control Engineering, ICECC 2019 - Phuket, Thailand
継続期間: 2019 4 132019 4 16

出版物シリーズ

名前ACM International Conference Proceeding Series

Conference

Conference2nd International Conference on Electronics, Communications and Control Engineering, ICECC 2019
国/地域Thailand
CityPhuket
Period19/4/1319/4/16

ASJC Scopus subject areas

  • 人間とコンピュータの相互作用
  • コンピュータ ネットワークおよび通信
  • コンピュータ ビジョンおよびパターン認識
  • ソフトウェア

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