Online segmentation of human motion for automated rehabilitation exercise analysis

Jonathan Feng Shun Lin, Dana Kulic

Research output: Contribution to journalArticleResearchpeer-review

79 Citations (Scopus)

Abstract

To enable automated analysis of rehabilitation movements, an approach for accurately identifying and segmenting movement repetitions is required. This paper proposes an approach for online, automated segmentation and identification of movement segments from continuous time-series data of human movement, obtained from body-mounted inertial measurement units or from motion capture data. The proposed approach uses a two-stage identification and recognition process, based on velocity features and stochastic modeling of each motion to be identified. In the first stage, motion segment candidates are identified based on a characteristic sequence of velocity features such as velocity peaks and zero velocity crossings. In the second stage, hidden Markov models are used to accurately identify segment locations from the identified candidates. The proposed approach is capable of online segmentation and identification, enabling interactive feedback in rehabilitation applications. The approach is validated on 20 healthy subjects and four rehabilitation patients performing rehabilitation movements, achieving segmentation accuracy of 87% with user specific templates and 79%-83% accuracy with user-independent templates.

Original languageEnglish
Pages (from-to)168-180
Number of pages13
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
Volume22
Issue number1
DOIs
Publication statusPublished - 2 May 2014
Externally publishedYes

Keywords

  • Feature extraction
  • Hidden Markov models
  • Motion analysis
  • Pattern recognition

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