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Leveraging IMU Data for Accurate Exercise Performance Classification and Musculoskeletal Injury Risk Screening |
Whelan, Darragh; O'Reilly, Martin; Huang, Bingquan; Giggins, Oonagh M.; Kechadi, Tahar; Caulfield, Brian
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38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Florida, United States of America, 17-20 August 2016 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. Science Foundation Ireland
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Keyword(s):
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Personal sensing |
Publication Date:
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2016 |
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Type:
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Other |
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Peer-Reviewed:
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Unknown |
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Language(s):
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English |
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Institution:
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University College Dublin |
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Publisher(s):
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IEEE |
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First Indexed:
2016-08-24 05:19:57 Last Updated:
2018-10-11 15:14:20 |
