Gesture recognition system using optical muscle deformation sensors

Satoshi Hosono, Shoji Nishimura, Ken Iwasaki, Emi Tamaki

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the 2nd International Conference on Electronics, Communications and Control Engineering, ICECC 2019
PublisherAssociation for Computing Machinery
Pages12-15
Number of pages4
ISBN (Electronic)9781450362634
DOIs
Publication statusPublished - 2019 Apr 13
Event2nd International Conference on Electronics, Communications and Control Engineering, ICECC 2019 - Phuket, Thailand
Duration: 2019 Apr 132019 Apr 16

Publication series

NameACM International Conference Proceeding Series

Conference

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

Fingerprint

Gesture recognition
Optical systems
Muscle
Augmented reality
Sensors
Virtual reality
Learning systems
Logistics
Experiments

Keywords

  • Hand gesture
  • Human activity recognition
  • Information interface

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Software

Cite this

Hosono, S., Nishimura, S., Iwasaki, K., & Tamaki, E. (2019). Gesture recognition system using optical muscle deformation sensors. In Proceedings of the 2nd International Conference on Electronics, Communications and Control Engineering, ICECC 2019 (pp. 12-15). (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3324033.3324037

Gesture recognition system using optical muscle deformation sensors. / Hosono, Satoshi; Nishimura, Shoji; Iwasaki, Ken; Tamaki, Emi.

Proceedings of the 2nd International Conference on Electronics, Communications and Control Engineering, ICECC 2019. Association for Computing Machinery, 2019. p. 12-15 (ACM International Conference Proceeding Series).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Hosono, S, Nishimura, S, Iwasaki, K & Tamaki, E 2019, Gesture recognition system using optical muscle deformation sensors. in Proceedings of the 2nd International Conference on Electronics, Communications and Control Engineering, ICECC 2019. ACM International Conference Proceeding Series, Association for Computing Machinery, pp. 12-15, 2nd International Conference on Electronics, Communications and Control Engineering, ICECC 2019, Phuket, Thailand, 19/4/13. https://doi.org/10.1145/3324033.3324037
Hosono S, Nishimura S, Iwasaki K, Tamaki E. Gesture recognition system using optical muscle deformation sensors. In Proceedings of the 2nd International Conference on Electronics, Communications and Control Engineering, ICECC 2019. Association for Computing Machinery. 2019. p. 12-15. (ACM International Conference Proceeding Series). https://doi.org/10.1145/3324033.3324037
Hosono, Satoshi ; Nishimura, Shoji ; Iwasaki, Ken ; Tamaki, Emi. / Gesture recognition system using optical muscle deformation sensors. Proceedings of the 2nd International Conference on Electronics, Communications and Control Engineering, ICECC 2019. Association for Computing Machinery, 2019. pp. 12-15 (ACM International Conference Proceeding Series).
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