Document Detail

Segmenting human motion for automated rehabilitation exercise analysis.
MedLine Citation:
PMID:  23366526     Owner:  NLM     Status:  MEDLINE    
This paper proposes an approach for the automated segmentation and identification of movement segments from continuous time series data of human movement, collected through motion capture of ambulatory sensors. The proposed approach uses a two stage identification and recognition process, based on velocity and stochastic modeling of each motion to be identified. In the first stage, motion segment candidates are identified based on a unique 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 approach is capable of on-line segmentation and identification, enabling interactive feedback in rehabilitation applications. The approach is validated on a rehabilitation movement dataset, and achieves a segmentation accuracy of 89%.
Jonathan Feng-Shun Lin; Dana Kulić
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Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference     Volume:  2012     ISSN:  1557-170X     ISO Abbreviation:  Conf Proc IEEE Eng Med Biol Soc     Publication Date:  2012  
Date Detail:
Created Date:  2013-01-31     Completed Date:  2013-08-26     Revised Date:  2014-08-21    
Medline Journal Info:
Nlm Unique ID:  101243413     Medline TA:  Conf Proc IEEE Eng Med Biol Soc     Country:  United States    
Other Details:
Languages:  eng     Pagination:  2881-4     Citation Subset:  IM    
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MeSH Terms
Exercise Therapy
Markov Chains
Movement / physiology*
Pattern Recognition, Automated

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

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