Gait episode identification based on wavelet feature clustering of spectrogram images

Yuwono, M; Su, SW; Moulton, BD; Nguyen, HT

Gait episode identification based on wavelet feature clustering of spectrogram images

Yuwono, M Su, SW Moulton, BD Nguyen, HT

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: Proceedings of the 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2012, pp. 2949 - 2952
Issue Date:: 2012-01

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Field	Value	Language
dc.contributor.author	Yuwono, M	en_US
dc.contributor.author	Su, SW	en_US
dc.contributor.author	Moulton, BD	en_US
dc.contributor.author	Nguyen, HT	en_US
dc.contributor.editor	Khoo, MCK	en_US
dc.date	2012-08-28	en_US
dc.date.issued	2012-01	en_US
dc.identifier.citation	Proceedings of the 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2012, pp. 2949 - 2952	en_US
dc.identifier.isbn	978-1-4577-1787-1	en_US
dc.identifier.uri	http://hdl.handle.net/10453/22398
dc.description.abstract	Automatic analysis of gait using kinematic sensors is a newly emerging area of research. We describe a new way to detect walking, and measure gait cadence, by using time-frequency signal processing together with spectrogram analysis of signals from a chest-worn inertial measurement unit (IMU). A pilot study of 11 participants suggests that this method is able to distinguish between walk and non-walk activities with up to 88.70% sensitivity and 97.70% specificity. Limitations of the method include instability associated with manual fine-tuning of local and global threshold levels.	en_US
dc.publisher	IEEE	en_US
dc.relation.ispartof	Proceedings of the 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society	en_US
dc.relation.ispartof	International Conference of the IEEE Engineering in Medicine and Biology Society	en_US
dc.relation.isbasedon	10.1109/EMBC.2012.6346582	en_US
dc.rights	© 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Gait	en_US
dc.subject.mesh	Algorithms	en_US
dc.subject.mesh	Neural Networks (Computer)	en_US
dc.title	Gait episode identification based on wavelet feature clustering of spectrogram images	en_US
dc.type	Conference Proceeding
utslib.location	USA	en_US
utslib.location.activity	San Diego, California, USA	en_US
utslib.for	0903 Biomedical Engineering	en_US
dc.location.activity	San Diego, California, USA	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/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
pubs.organisational-group	/University of Technology Sydney/Strength - GEVI - Green Energy and Vehicle Innovations
utslib.copyright.status	closed_access
pubs.consider-herdc	true	en_US
pubs.place-of-publication	USA	en_US
pubs.start-date	2012-08-28	en_US

Abstract:

Automatic analysis of gait using kinematic sensors is a newly emerging area of research. We describe a new way to detect walking, and measure gait cadence, by using time-frequency signal processing together with spectrogram analysis of signals from a chest-worn inertial measurement unit (IMU). A pilot study of 11 participants suggests that this method is able to distinguish between walk and non-walk activities with up to 88.70% sensitivity and 97.70% specificity. Limitations of the method include instability associated with manual fine-tuning of local and global threshold levels.

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http://hdl.handle.net/10453/22398