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  5. Leveraging IMU Data for Accurate Exercise Performance Classification and Musculoskeletal Injury Risk Screening
 
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Leveraging IMU Data for Accurate Exercise Performance Classification and Musculoskeletal Injury Risk Screening

Author(s)
Whelan, Darragh  
O'Reilly, Martin  
Huang, Bingquan  
Giggins, Oonagh M.  
Kechadi, Tahar  
Caulfield, Brian  
Uri
http://hdl.handle.net/10197/7831
Date Issued
2016-08-20
Date Available
2016-08-23T14:09:24Z
Abstract
Inertial measurement units (IMUs) are becoming increasingly prevalent as a method for low cost and portable biomechanical analysis. However, to date they have not tended to be accepted into routine clinical practice. This is often due to the disconnect between translating the data collected by the sensors into meaningful and actionable information for end users. This paper outlines the work completed by our group in attempting to achieve this. We discuss the conceptual framework involved in our work, the methodological approach taken in analysing sensor signals and discuss possible application models. The work completed by our group indicates that IMU based systems have the potential to bridge the gap between laboratory and clinical movement analysis. Future work will focus on collecting a diverse range of movement data and using more sophisticated data analysis techniques to refine systems.
Sponsorship
Science Foundation Ireland
Type of Material
Conference Publication
Publisher
IEEE
Copyright (Published Version)
2016 IEEE
Subjects

Personal sensing

DOI
10.1109/EMBC.2016.7590788
Web versions
http://embc.embs.org/2016/
Language
English
Status of Item
Peer reviewed
Conference Details
38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Florida, United States of America, 17-20 August 2016
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
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insight_publication.pdf

Size

642.67 KB

Format

Adobe PDF

Checksum (MD5)

a07c53e79705928e2603d09dc6cf6364

Owning collection
Insight Research Collection
Mapped collections
Computer Science Research Collection

Item descriptive metadata is released under a CC-0 (public domain) license: https://creativecommons.org/public-domain/cc0/.
All other content is subject to copyright.

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