@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.; null ; 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 = "United States",
}
