Hand gesture recognition for post-stroke rehabilitation using leap motion

Wen Jeng Li, Chia Yeh Hsieh, Li Fong Lin, Woei Chyn Chu

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

21 Citations (Scopus)

Abstract

In order to enhance and/or improve recovery after stroke, rehabilitation needs to start early and be monitored by continuous and recurrent long-Term interventions in the clinic and home setting. The elderly is a high risk stroke group with advancing age, resulting in increasing demand of strengthened resource of hospitals and physiotherapist. The residential rehabilitation for stroke patients would effectively relieve shortages of medical resources. However, the residential rehabilitation for stroke patients faces with the lack of professional guidance, and physiotherapist cannot monitor the rehabilitation progress of stroke patients. These problems may lead to additional harm or deteriorate rehabilitation progress. In order to solve this problem, we develop a hand gesture recognition algorithm devoted to monitor the seven gestures for residential rehabilitation of the post-stroke patients. The gestures were performed by seventeen healthy young subjects. The results were assessed by k-fold cross validation method. The results show that the proposed hand gesture recognition algorithm using multi-class SVM and k-NN classifier achieve accuracy of 97.29% and 97.71%, respectively.

Original languageEnglish
Title of host publicationProceedings of the 2017 IEEE International Conference on Applied System Innovation
Subtitle of host publicationApplied System Innovation for Modern Technology, ICASI 2017
EditorsTeen-Hang Meen, Artde Donald Kin-Tak Lam, Stephen D. Prior
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages386-388
Number of pages3
ISBN (Electronic)9781509048977
DOIs
Publication statusPublished - Jul 21 2017
Event2017 IEEE International Conference on Applied System Innovation, ICASI 2017 - Sapporo, Japan
Duration: May 13 2017May 17 2017

Conference

Conference2017 IEEE International Conference on Applied System Innovation, ICASI 2017
CountryJapan
CitySapporo
Period5/13/175/17/17

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Keywords

  • Gesture recognition
  • Leap motion
  • Machine learning
  • Stroke rehabilitation

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Hardware and Architecture
  • Safety, Risk, Reliability and Quality
  • Mechanical Engineering
  • Media Technology
  • Health Informatics
  • Instrumentation

Cite this

Li, W. J., Hsieh, C. Y., Lin, L. F., & Chu, W. C. (2017). Hand gesture recognition for post-stroke rehabilitation using leap motion. In T-H. Meen, A. D. K-T. Lam, & S. D. Prior (Eds.), Proceedings of the 2017 IEEE International Conference on Applied System Innovation: Applied System Innovation for Modern Technology, ICASI 2017 (pp. 386-388). [7988433] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASI.2017.7988433