Human motion identification for rehabilitation exercise assessment of knee osteoarthritis

Po Chun Huang; Kai Chun Liu; Chia Yeh Hsieh; Chia Tai Chan

doi:10.1109/ICASI.2017.7988396

Human motion identification for rehabilitation exercise assessment of knee osteoarthritis

Po Chun Huang
, Kai Chun Liu
, Chia Yeh Hsieh
, Chia Tai Chan

生物醫學工程學系

研究成果 › 同行評審

11 引文斯高帕斯（Scopus）

摘要

Osteoarthritis (OA) is one of the majority of chronic lower limb musculoskeletal conditions, affecting approximately 15% of the population. Rehabilitation exercise has been considered as a common and essential medical treatment for mild to moderate stages of knee OA. However, there are some issues and challenges should be tackled while OA patient performs rehabilitation exercise without supervision of therapist, such as improperly implement rehabilitation exercise and patient adherence. The objective of this study is to propose a machine learning-based human motion identification system to automatically classify rehabilitation types and the motion states. The overall accuracy for types recognition is 100% and for motion identification is 97.7%. The results show that the feasibility of the proposed human motion identification algorithm for home-based rehabilitation.

原文	English
主出版物標題	Proceedings of the 2017 IEEE International Conference on Applied System Innovation
主出版物子標題	Applied System Innovation for Modern Technology, ICASI 2017
編輯	Teen-Hang Meen, Artde Donald Kin-Tak Lam, Stephen D. Prior
發行者	Institute of Electrical and Electronics Engineers Inc.
頁面	246-249
頁數	4
ISBN（電子）	9781509048977
DOIs	https://doi.org/10.1109/ICASI.2017.7988396
出版狀態	Published - 21 7月 2017
事件	2017 IEEE International Conference on Applied System Innovation, ICASI 2017 - Sapporo, 日本持續時間: 13 5月 2017 → 17 5月 2017

出版系列

名字	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
國家/地區	日本
城市	Sapporo
期間	13/05/17 → 17/05/17

UN SDG

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SDG 3 良好的健康和福祉

存取文件

10.1109/ICASI.2017.7988396

其它文件與鏈接

Link to publication in Scopus

引用此

Huang, P. C., Liu, K. C., Hsieh, C. Y., & Chan, C. T. (2017). Human motion identification for rehabilitation exercise assessment of knee osteoarthritis. 於 T.-H. Meen, A. D. K.-T. Lam, & S. D. Prior (編輯), Proceedings of the 2017 IEEE International Conference on Applied System Innovation: Applied System Innovation for Modern Technology, ICASI 2017 (頁 246-249). 文章 7988396 (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.2017.7988396

Huang, Po Chun ; Liu, Kai Chun ; Hsieh, Chia Yeh 等. / Human motion identification for rehabilitation exercise assessment of knee osteoarthritis. Proceedings of the 2017 IEEE International Conference on Applied System Innovation: Applied System Innovation for Modern Technology, ICASI 2017. 編輯 / Teen-Hang Meen ; Artde Donald Kin-Tak Lam ; Stephen D. Prior. Institute of Electrical and Electronics Engineers Inc., 2017. 頁 246-249 (Proceedings of the 2017 IEEE International Conference on Applied System Innovation: Applied System Innovation for Modern Technology, ICASI 2017).

@inproceedings{67cc688487ec489c96883a1d73681308,

title = "Human motion identification for rehabilitation exercise assessment of knee osteoarthritis",

abstract = "Osteoarthritis (OA) is one of the majority of chronic lower limb musculoskeletal conditions, affecting approximately 15\% of the population. Rehabilitation exercise has been considered as a common and essential medical treatment for mild to moderate stages of knee OA. However, there are some issues and challenges should be tackled while OA patient performs rehabilitation exercise without supervision of therapist, such as improperly implement rehabilitation exercise and patient adherence. The objective of this study is to propose a machine learning-based human motion identification system to automatically classify rehabilitation types and the motion states. The overall accuracy for types recognition is 100\% and for motion identification is 97.7\%. The results show that the feasibility of the proposed human motion identification algorithm for home-based rehabilitation.",

keywords = "Human motion identification, Machine learning, Wearable sensor",

author = "Huang, \{Po Chun\} and Liu, \{Kai Chun\} and Hsieh, \{Chia Yeh\} and Chan, \{Chia Tai\}",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE International Conference on Applied System Innovation, ICASI 2017 ; Conference date: 13-05-2017 Through 17-05-2017",

year = "2017",

month = jul,

day = "21",

doi = "10.1109/ICASI.2017.7988396",

language = "English",

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

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "246--249",

editor = "Teen-Hang Meen and Lam, \{Artde Donald Kin-Tak\} and Prior, \{Stephen D.\}",

booktitle = "Proceedings of the 2017 IEEE International Conference on Applied System Innovation",

address = "美國",

}

Huang, PC, Liu, KC, Hsieh, CY & Chan, CT 2017, Human motion identification for rehabilitation exercise assessment of knee osteoarthritis. 於 T-H Meen, ADK-T Lam & SD Prior (編輯), Proceedings of the 2017 IEEE International Conference on Applied System Innovation: Applied System Innovation for Modern Technology, ICASI 2017., 7988396, 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., 頁 246-249, 2017 IEEE International Conference on Applied System Innovation, ICASI 2017, Sapporo, 日本, 13/05/17. https://doi.org/10.1109/ICASI.2017.7988396

Human motion identification for rehabilitation exercise assessment of knee osteoarthritis. / Huang, Po Chun; Liu, Kai Chun; Hsieh, Chia Yeh 等.
Proceedings of the 2017 IEEE International Conference on Applied System Innovation: Applied System Innovation for Modern Technology, ICASI 2017. 編輯 / Teen-Hang Meen; Artde Donald Kin-Tak Lam; Stephen D. Prior. Institute of Electrical and Electronics Engineers Inc., 2017. p. 246-249 7988396 (Proceedings of the 2017 IEEE International Conference on Applied System Innovation: Applied System Innovation for Modern Technology, ICASI 2017).

研究成果 › 同行評審

TY - GEN

T1 - Human motion identification for rehabilitation exercise assessment of knee osteoarthritis

AU - Huang, Po Chun

AU - Liu, Kai Chun

AU - Hsieh, Chia Yeh

AU - Chan, Chia Tai

PY - 2017/7/21

Y1 - 2017/7/21

N2 - Osteoarthritis (OA) is one of the majority of chronic lower limb musculoskeletal conditions, affecting approximately 15% of the population. Rehabilitation exercise has been considered as a common and essential medical treatment for mild to moderate stages of knee OA. However, there are some issues and challenges should be tackled while OA patient performs rehabilitation exercise without supervision of therapist, such as improperly implement rehabilitation exercise and patient adherence. The objective of this study is to propose a machine learning-based human motion identification system to automatically classify rehabilitation types and the motion states. The overall accuracy for types recognition is 100% and for motion identification is 97.7%. The results show that the feasibility of the proposed human motion identification algorithm for home-based rehabilitation.

AB - Osteoarthritis (OA) is one of the majority of chronic lower limb musculoskeletal conditions, affecting approximately 15% of the population. Rehabilitation exercise has been considered as a common and essential medical treatment for mild to moderate stages of knee OA. However, there are some issues and challenges should be tackled while OA patient performs rehabilitation exercise without supervision of therapist, such as improperly implement rehabilitation exercise and patient adherence. The objective of this study is to propose a machine learning-based human motion identification system to automatically classify rehabilitation types and the motion states. The overall accuracy for types recognition is 100% and for motion identification is 97.7%. The results show that the feasibility of the proposed human motion identification algorithm for home-based rehabilitation.

KW - Human motion identification

KW - Machine learning

KW - Wearable sensor

UR - https://www.scopus.com/pages/publications/85028527595

U2 - 10.1109/ICASI.2017.7988396

DO - 10.1109/ICASI.2017.7988396

M3 - Conference contribution

AN - SCOPUS:85028527595

T3 - Proceedings of the 2017 IEEE International Conference on Applied System Innovation: Applied System Innovation for Modern Technology, ICASI 2017

SP - 246

EP - 249

BT - Proceedings of the 2017 IEEE International Conference on Applied System Innovation

A2 - Meen, Teen-Hang

A2 - Lam, Artde Donald Kin-Tak

A2 - Prior, Stephen D.

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2017 IEEE International Conference on Applied System Innovation, ICASI 2017

Y2 - 13 May 2017 through 17 May 2017

ER -

Huang PC, Liu KC, Hsieh CY, Chan CT. Human motion identification for rehabilitation exercise assessment of knee osteoarthritis. 於 Meen TH, Lam ADKT, Prior SD, 編輯, 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. 246-249. 7988396. (Proceedings of the 2017 IEEE International Conference on Applied System Innovation: Applied System Innovation for Modern Technology, ICASI 2017). doi: 10.1109/ICASI.2017.7988396

Human motion identification for rehabilitation exercise assessment of knee osteoarthritis

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