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  5. Segmentation of shoulder rehabilitation exercises for single and multiple inertial sensor systems
 
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Segmentation of shoulder rehabilitation exercises for single and multiple inertial sensor systems

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Author(s)
Brennan, Louise 
Bevilacqua, Antonio 
Kechadi, Tahar 
Caulfield, Brian 
Uri
http://hdl.handle.net/10197/11636
Date Issued
01 January 2020
Date Available
20T11:25:14Z October 2020
Abstract
Introduction:Digital home rehabilitation systems require accurate segmentation methods to provide appropriate feedback on repetition counting and exercise technique. Current segmentation methods are not suitable for clinical use; they are not highly accurate or require multiple sensors, which creates usability problems. We propose a model for accurately segmenting inertial measurement unit data for shoulder rehabilitation exercises. This study aims to use inertial measurement unit data to train and test a machine learning segmentation model for single- and multiple-inertial measurement unit systems and to identify the optimal single-sensor location. Methods:A focus group of specialist physiotherapists selected the exercises, which were performed by participants wearing inertial measurement units on the wrist, arm and scapula. We applied a novel machine learning based segmentation technique involving a convolutional classifier and Finite State Machine to the inertial measurement unit data. An accuracy score was calculated for each possible single- or multiple-sensor system. Results:The wrist inertial measurement unit was chosen as the optimal single-sensor location for future system development (mean overall accuracy 0.871). Flexion and abduction based exercises mostly could be segmented with high accuracy, but scapular movement exercises had poor accuracy. Conclusion:A wrist-worn single inertial measurement unit system can accurately segment shoulder exercise repetitions; however, accuracy varies depending on characteristics of the exercise.
Sponsorship
European Commission Horizon 2020
Type of Material
Journal Article
Publisher
SAGE
Journal
Journal of Rehabilitation and Assistive Technologies Engineering
Volume
7
Start Page
1
End Page
10
Copyright (Published Version)
2020 the Authors
Keywords
  • Segmentation

  • Home rehabilitation

  • Inertial measurement ...

  • Machine learning

  • Shoulder

DOI
10.1177/2055668320915377
Language
English
Status of Item
Peer reviewed
ISSN
2055-6683
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
Owning collection
Public Health, Physiotherapy and Sports Science Research Collection
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Acquisition Date
Feb 6, 2023
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