Human motion identification for rehabilitation exercise assessment of knee osteoarthritis

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Scopus citations

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.

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.
Pages246-249
Number of pages4
ISBN (Electronic)9781509048977
DOIs
StatePublished - 21 Jul 2017
Event2017 IEEE International Conference on Applied System Innovation, ICASI 2017 - Sapporo, Japan
Duration: 13 May 201717 May 2017

Publication series

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

Conference

Conference2017 IEEE International Conference on Applied System Innovation, ICASI 2017
Country/TerritoryJapan
CitySapporo
Period13/05/1717/05/17

Keywords

  • Human motion identification
  • Machine learning
  • Wearable sensor

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