Human motion segmentation by data point classification

Jonathan Feng-Shun Lin, Vladimir Joukov, Dana Kulic

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

13 Citations (Scopus)

Abstract

Contemporary physiotherapy and rehabilitation practice uses subjective measures for motion evaluation and requires time-consuming supervision. Algorithms that can accurately segment patient movement would provide valuable data for progress tracking and on-line patient feedback. In this paper, we propose a two-class classifier approach to label each data point in the patient movement data as either a segment point or a non-segment point. The proposed technique was applied to 20 healthy subjects performing lower body rehabilitation exercises, and achieves a segmentation accuracy of 82%.

Original languageEnglish
Title of host publication2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
EditorsJeff Duerk, Jim Ji
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages9-13
Number of pages5
ISBN (Electronic)9781424479290
ISBN (Print)9781424479276
DOIs
Publication statusPublished - 6 Nov 2014
Externally publishedYes
EventInternational Conference of the IEEE Engineering in Medicine and Biology Society 2014 - Sheraton Chicago Hotel and Towers, Chicago, United States of America
Duration: 26 Aug 201430 Aug 2014
Conference number: 36th
https://ieeexplore-ieee-org.ezproxy.lib.monash.edu.au/xpl/conhome/6923026/proceeding (Proceedings)
https://web.archive.org/web/20140331105330/http://embc.embs.org/2014/?page_id=120 (Website)

Conference

ConferenceInternational Conference of the IEEE Engineering in Medicine and Biology Society 2014
Abbreviated titleEMBC 2014
CountryUnited States of America
CityChicago
Period26/08/1430/08/14
Internet address

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