Abnormalities detection of IMU based on PCA in motion monitoring

Zhou, J; Su, S; Guo, AH; Chen, WD

Abnormalities detection of IMU based on PCA in motion monitoring

Zhou, J Su, S

Guo, AH Chen, WD

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Publication Type:: Journal Article
Citation:: Applied Mechanics and Materials, 2012, 224 pp. 533 - 538
Issue Date:: 2012-12-14

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Field	Value	Language
dc.contributor.author	Zhou, J	en_US
dc.contributor.author	Su, S https://orcid.org/0000-0002-5720-8852	en_US
dc.contributor.author	Guo, AH	en_US
dc.contributor.author	Chen, WD	en_US
dc.date.issued	2012-12-14	en_US
dc.identifier.citation	Applied Mechanics and Materials, 2012, 224 pp. 533 - 538	en_US
dc.identifier.isbn	9783037855058	en_US
dc.identifier.issn	1660-9336	en_US
dc.identifier.uri	http://hdl.handle.net/10453/23177
dc.description.abstract	Inertial measurement units (IMU) are used as an affordable and effective remote measurement method for health monitoring in body sensor networks (BSNs) based on tracking people's daily motions and activities. These inertial sensors are mostly micro-electro-mechanical systems with a combination of multi-axis combinations of precision gyroscopes, accelerometers, and magnetometers to sense multiple degrees of freedom (DoF).Unfortunately in the process of motion monitoring actual sensor outputs may contain some abnormalities, which might result in the misinterpretations of activities. In this paper, we use Principal component analysis (PCA) combined with Hotelling's T2 and SPE statistic to detect abnormal data in the process of motion monitoring with IMU to ensure the reliability and accuracy in application. The simulated results prove this method is effective and feasible. © (2012) Trans Tech Publications, Switzerland.	en_US
dc.relation.ispartof	Applied Mechanics and Materials	en_US
dc.relation.isbasedon	10.4028/www.scientific.net/AMM.224.533	en_US
dc.title	Abnormalities detection of IMU based on PCA in motion monitoring	en_US
dc.type	Journal Article
utslib.citation.volume	224	en_US
utslib.for	0999 Other Engineering	en_US
utslib.for	09 Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	224	en_US

Abstract:

Inertial measurement units (IMU) are used as an affordable and effective remote measurement method for health monitoring in body sensor networks (BSNs) based on tracking people's daily motions and activities. These inertial sensors are mostly micro-electro-mechanical systems with a combination of multi-axis combinations of precision gyroscopes, accelerometers, and magnetometers to sense multiple degrees of freedom (DoF).Unfortunately in the process of motion monitoring actual sensor outputs may contain some abnormalities, which might result in the misinterpretations of activities. In this paper, we use Principal component analysis (PCA) combined with Hotelling's T2 and SPE statistic to detect abnormal data in the process of motion monitoring with IMU to ensure the reliability and accuracy in application. The simulated results prove this method is effective and feasible. © (2012) Trans Tech Publications, Switzerland.

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http://hdl.handle.net/10453/23177