Gait cycle spectrogram analysis using a torso-attached inertial sensor
- 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. 6539 - 6542
- Issue Date:
- 2012-01
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Filename | Description | Size | |||
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2011007617OK.pdf | 1.89 MB | Adobe PDF |
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Full metadata record
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. 6539 - 6542 | en_US |
dc.identifier.isbn | 978-1-4577-1787-1 | en_US |
dc.identifier.uri | http://hdl.handle.net/10453/22400 | |
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 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.2012.6347492 | 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 | Monitoring, Ambulatory | en_US |
dc.subject.mesh | Gait | en_US |
dc.subject.mesh | Walking | en_US |
dc.subject.mesh | Models, Statistical | en_US |
dc.subject.mesh | Sensitivity and Specificity | en_US |
dc.subject.mesh | Normal Distribution | en_US |
dc.subject.mesh | Reproducibility of Results | en_US |
dc.subject.mesh | Algorithms | en_US |
dc.subject.mesh | Acceleration | en_US |
dc.subject.mesh | Automatic Data Processing | en_US |
dc.subject.mesh | Image Processing, Computer-Assisted | en_US |
dc.subject.mesh | Signal Processing, Computer-Assisted | en_US |
dc.subject.mesh | Software | en_US |
dc.subject.mesh | Aged | en_US |
dc.subject.mesh | Middle Aged | en_US |
dc.subject.mesh | Biomechanical Phenomena | en_US |
dc.title | Gait cycle spectrogram analysis using a torso-attached inertial sensor | 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:
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 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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