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

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Title: Leveraging IMU Data for Accurate Exercise Performance Classification and Musculoskeletal Injury Risk Screening
Authors: Whelan, Darragh
O'Reilly, Martin
Huang, Bingquan
Giggins, Oonagh M.
Kechadi, Tahar
Caulfield, Brian
Permanent link: http://hdl.handle.net/10197/7831
Date: 20-Aug-2016
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.
Funding Details: Science Foundation Ireland
Type of material: Conference Publication
Publisher: IEEE
Copyright (published version): 2016 IEEE
Keywords: Personal sensing
DOI: 10.1109/EMBC.2016.7590788
Language: en
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
Appears in Collections:Computer Science Research Collection
Insight Research Collection

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