Human motion segmentation by data point classification

Jonathan Feng-Shun Lin; Vladimir Joukov; Dana Kulic

doi:10.1109/EMBC.2014.6943516

Human motion segmentation by data point classification

Jonathan Feng-Shun Lin, Vladimir Joukov, Dana Kulic

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

15 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 language	English
Title of host publication	2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Editors	Jeff Duerk, Jim Ji
Place of Publication	Piscataway NJ USA
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	9-13
Number of pages	5
ISBN (Electronic)	9781424479290
ISBN (Print)	9781424479276
DOIs	https://doi.org/10.1109/EMBC.2014.6943516
Publication status	Published - 6 Nov 2014
Externally published	Yes
Event	International Conference of the IEEE Engineering in Medicine and Biology Society 2014 - Sheraton Chicago Hotel and Towers, Chicago, United States of America Duration: 26 Aug 2014 → 30 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

Conference	International Conference of the IEEE Engineering in Medicine and Biology Society 2014
Abbreviated title	EMBC 2014
Country/Territory	United States of America
City	Chicago
Period	26/08/14 → 30/08/14
Internet address	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)

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10.1109/EMBC.2014.6943516

Cite this

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title = "Human motion segmentation by data point classification",

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%.",

author = "Lin, {Jonathan Feng-Shun} and Vladimir Joukov and Dana Kulic",

year = "2014",

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doi = "10.1109/EMBC.2014.6943516",

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editor = "Jeff Duerk and Jim Ji",

booktitle = "2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

address = "United States of America",

note = "International Conference of the IEEE Engineering in Medicine and Biology Society 2014, EMBC 2014 ; Conference date: 26-08-2014 Through 30-08-2014",

url = "https://ieeexplore-ieee-org.ezproxy.lib.monash.edu.au/xpl/conhome/6923026/proceeding, https://web.archive.org/web/20140331105330/http://embc.embs.org/2014/?page_id=120",

}

Lin, JF-S, Joukov, V & Kulic, D 2014, Human motion segmentation by data point classification. in J Duerk & J Ji (eds), 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, Institute of Electrical and Electronics Engineers, Piscataway NJ USA, pp. 9-13, International Conference of the IEEE Engineering in Medicine and Biology Society 2014, Chicago, Illinois, United States of America, 26/08/14. https://doi.org/10.1109/EMBC.2014.6943516

Human motion segmentation by data point classification. / Lin, Jonathan Feng-Shun; Joukov, Vladimir; Kulic, Dana.

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. ed. / Jeff Duerk; Jim Ji. Piscataway NJ USA : IEEE, Institute of Electrical and Electronics Engineers, 2014. p. 9-13.

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

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AB - 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%.

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Human motion segmentation by data point classification

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