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 Xplore
Publication Type:: Conference Proceeding
Citation:: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2012, pp. 2949 - 2952
Issue Date:: 2012-12-14

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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.date	2012-08-28	en_US
dc.date.issued	2012-12-14	en_US
dc.identifier.citation	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2012, pp. 2949 - 2952	en_US
dc.identifier.isbn	9781424441198	en_US
dc.identifier.issn	1557-170X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/119568
dc.description.abstract	Measurement of gait parameters can provide important information about a person's health and safety. Automatic analysis of gait using kinematic sensors is a newly emerging area of research. We propose a new approach to detect gait episodes using Neural Network and and clustering of wavelet-decomposed spectrogram images. Signals from a chest-worn inertial measurement unit (IMU) is processed using Explicit Complementary Filter (ECF) to estimate and track torso angle. Using the feature obtained from wavelet decomposition of spectrogram images, we use an Augmented Radial Basis Neural Network (ARBF) to classify walking episodes. Cluster centroids of ARBF are optimized using Rapid Cluster Estimation (RCE). A pilot study of 11 participants suggests that our approach is able to distinguish between walk and non-walk activities with up to 85.71% sensitivity and 91.34% specificity. © 2012 IEEE.	en_US
dc.publisher	IEEE Xplore	en_US
dc.relation.ispartof	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS	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.rights	info:eu-repo/semantics/closedAccess
dc.title	Gait episode identification based on wavelet feature clustering of spectrogram images	en_US
dc.type	Conference Proceeding
utslib.location.activity	San Diego, CA, USA	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	0903 Biomedical 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/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.declined	2017-11-01T11:32:17.130+1100
pubs.consider-herdc	false	en_US
pubs.deleted	2017-11-01T11:32:17.130+1100
pubs.finish-date	2012-09-01	en_US
pubs.publication-status	Published	en_US
pubs.start-date	2012-08-28	en_US

Abstract:

Measurement of gait parameters can provide important information about a person's health and safety. Automatic analysis of gait using kinematic sensors is a newly emerging area of research. We propose a new approach to detect gait episodes using Neural Network and and clustering of wavelet-decomposed spectrogram images. Signals from a chest-worn inertial measurement unit (IMU) is processed using Explicit Complementary Filter (ECF) to estimate and track torso angle. Using the feature obtained from wavelet decomposition of spectrogram images, we use an Augmented Radial Basis Neural Network (ARBF) to classify walking episodes. Cluster centroids of ARBF are optimized using Rapid Cluster Estimation (RCE). A pilot study of 11 participants suggests that our approach is able to distinguish between walk and non-walk activities with up to 85.71% sensitivity and 91.34% specificity. © 2012 IEEE.

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http://hdl.handle.net/10453/119568