Document Detail


Multi-label classification for the analysis of human motion quality.
MedLine Citation:
PMID:  23366363     Owner:  NLM     Status:  In-Data-Review    
Abstract/OtherAbstract:
Knowing how well an activity is performed is important for home rehabilitation. We would like to not only know if a motion is being performed correctly, but also in what way the motion is incorrect so that we may provide feedback to the user. This paper describes methods for assessing human motion quality using body-worn tri-axial accelerometers and gyroscopes. We use multi-label classifiers to detect subtle errors in exercise performances of eight individuals with knee osteoarthritis, a degenerative disease of the cartilage. We present results obtained using various machine learning methods with decision tree base classifiers. The classifier can detect classes in multi-label data with 75% sensitivity, 90% specificity and 80% accuracy. The methods presented here form the basis for an at-home rehabilitation device that will recognize errors in patient exercise performance, provide appropriate feedback on the performance, and motivate the patient to continue the prescribed regimen.
Authors:
Portia E Taylor; Gustavo J M Almeida; Jessica K Hodgins; Takeo Kanade
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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. Conference     Volume:  2012     ISSN:  1557-170X     ISO Abbreviation:  Conf Proc IEEE Eng Med Biol Soc     Publication Date:  2012 Aug 
Date Detail:
Created Date:  2013-01-31     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  101243413     Medline TA:  Conf Proc IEEE Eng Med Biol Soc     Country:  United States    
Other Details:
Languages:  eng     Pagination:  2214-8     Citation Subset:  IM    
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