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  5. The limb movement analysis of rehabilitation exercises using wearable inertial sensors
 
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The limb movement analysis of rehabilitation exercises using wearable inertial sensors

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Author(s)
Huang, Bingquan 
Giggins, Oonagh M. 
Kechadi, Tahar 
Caulfield, Brian 
Uri
http://hdl.handle.net/10197/8225
Date Issued
20 August 2016
Date Available
19T17:42:08Z December 2016
Abstract
Due to no supervision of a therapist in home based exercise programs, inertial sensor based feedback systems which can accurately assess movement repetitions are urgently required. The synchronicity and the degrees of freedom both show that one movement might resemble another movement signal which is mixed in with another not precisely defined movement. Therefore, the data and feature selections are important for movement analysis. This paper explores the data and feature selection for the limb movement analysis of rehabilitation exercises. The results highlight that the classification accuracy is very sensitive to the mount location of the sensors. The results show that the use of 2 or 3 sensor units, the combination of acceleration and gyroscope data, and the feature sets combined by the statistical feature set with another type of feature, can significantly improve the classification accuracy rates. The results illustrate that acceleration data is more effective than gyroscope data for most of the movement analysis.
Type of Material
Conference Publication
Publisher
IEEE
Copyright (Published Version)
2016 IEEE
Keywords
  • Personal sensing

  • Feature extraction

  • Signal classification...

  • Accelerometers

  • Gait analysis

DOI
10.1109/EMBC.2016.7591773
Language
English
Status of Item
Peer reviewed
Description
2016 IEEE 38th Annual Conference on Engineering in Medicine and Biology Society, Orlando, Florida, USA, 16-20 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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