Detecting changes in human motion using stochastic distance measures

Muhammad Choudry, Matthew Pillar, Tyson Beach, Dana Kulic, Jack P. Callaghan

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

2 Citations (Scopus)

Abstract

We propose a stochastic framework to analyze and compare differences in human motions for applications in injury prevention, rehabilitation, sports training and performance research. Human motions are modeled as Hidden Markov Models and the differences between the motions are measured using the Kullback-Leibler distance metric. The distance metric is recomputed with degrees of freedom excluded to determine which degree of freedom most influences the difference between a set of motions. The proposed system is tested on a human motion dataset consisting of lifting movements under differing load weights and ankle bracing conditions. Results indicate that the algorithm is capable of successfully determining which joints are impacted and ranking them according to importance.

Original languageEnglish
Title of host publication33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages3475-3478
Number of pages4
ISBN (Print)9781424441211
DOIs
Publication statusPublished - 26 Dec 2011
Externally publishedYes
EventInternational Conference of the IEEE Engineering in Medicine and Biology Society 2011 - Boston Marriott Copley Place Hotel, Boston, United States of America
Duration: 30 Aug 20113 Sep 2011
Conference number: 33rd

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (Print)1557-170X

Conference

ConferenceInternational Conference of the IEEE Engineering in Medicine and Biology Society 2011
Abbreviated titleEMBC 2011
CountryUnited States of America
CityBoston
Period30/08/113/09/11

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