Hand gesture recognition for post-stroke rehabilitation using leap motion

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

doi:10.1109/ICASI.2017.7988433

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 proceeding › Conference contribution

67 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 language	English
Title of host publication	Proceedings of the 2017 IEEE International Conference on Applied System Innovation
Subtitle of host publication	Applied System Innovation for Modern Technology, ICASI 2017
Editors	Teen-Hang Meen, Artde Donald Kin-Tak Lam, Stephen D. Prior
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	386-388
Number of pages	3
ISBN (Electronic)	9781509048977
DOIs	https://doi.org/10.1109/ICASI.2017.7988433
Publication status	Published - Jul 21 2017
Event	2017 IEEE International Conference on Applied System Innovation, ICASI 2017 - Sapporo, Japan Duration: May 13 2017 → May 17 2017

Publication series

Name	Proceedings of the 2017 IEEE International Conference on Applied System Innovation: Applied System Innovation for Modern Technology, ICASI 2017

Conference

Conference	2017 IEEE International Conference on Applied System Innovation, ICASI 2017
Country/Territory	Japan
City	Sapporo
Period	5/13/17 → 5/17/17

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

Access to Document

10.1109/ICASI.2017.7988433

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). Article 7988433 (Proceedings of the 2017 IEEE International Conference on Applied System Innovation: Applied System Innovation for Modern Technology, ICASI 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASI.2017.7988433

Hand gesture recognition for post-stroke rehabilitation using leap motion. / Li, Wen Jeng; Hsieh, Chia Yeh; Lin, Li Fong et al.
Proceedings of the 2017 IEEE International Conference on Applied System Innovation: Applied System Innovation for Modern Technology, ICASI 2017. ed. / Teen-Hang Meen; Artde Donald Kin-Tak Lam; Stephen D. Prior. Institute of Electrical and Electronics Engineers Inc., 2017. p. 386-388 7988433 (Proceedings of the 2017 IEEE International Conference on Applied System Innovation: Applied System Innovation for Modern Technology, ICASI 2017).

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

Li, WJ, Hsieh, CY, Lin, LF & Chu, WC 2017, Hand gesture recognition for post-stroke rehabilitation using leap motion. in T-H Meen, ADK-T Lam & SD Prior (eds), Proceedings of the 2017 IEEE International Conference on Applied System Innovation: Applied System Innovation for Modern Technology, ICASI 2017., 7988433, Proceedings of the 2017 IEEE International Conference on Applied System Innovation: Applied System Innovation for Modern Technology, ICASI 2017, Institute of Electrical and Electronics Engineers Inc., pp. 386-388, 2017 IEEE International Conference on Applied System Innovation, ICASI 2017, Sapporo, Japan, 5/13/17. https://doi.org/10.1109/ICASI.2017.7988433

Li WJ, Hsieh CY, Lin LF, Chu WC. Hand gesture recognition for post-stroke rehabilitation using leap motion. In Meen TH, Lam ADKT, Prior SD, editors, Proceedings of the 2017 IEEE International Conference on Applied System Innovation: Applied System Innovation for Modern Technology, ICASI 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 386-388. 7988433. (Proceedings of the 2017 IEEE International Conference on Applied System Innovation: Applied System Innovation for Modern Technology, ICASI 2017). doi: 10.1109/ICASI.2017.7988433

Li, Wen Jeng ; Hsieh, Chia Yeh ; Lin, Li Fong et al. / Hand gesture recognition for post-stroke rehabilitation using leap motion. Proceedings of the 2017 IEEE International Conference on Applied System Innovation: Applied System Innovation for Modern Technology, ICASI 2017. editor / Teen-Hang Meen ; Artde Donald Kin-Tak Lam ; Stephen D. Prior. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 386-388 (Proceedings of the 2017 IEEE International Conference on Applied System Innovation: Applied System Innovation for Modern Technology, ICASI 2017).

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AU - Hsieh, Chia Yeh

AU - Lin, Li Fong

AU - Chu, Woei Chyn

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Y1 - 2017/7/21

N2 - 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.

AB - 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.

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KW - Leap motion

KW - Machine learning

KW - Stroke rehabilitation

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Hand gesture recognition for post-stroke rehabilitation using leap motion

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