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

Title: Segmentation of shoulder rehabilitation exercises for single and multiple inertial sensor systems
Authors: Brennan, LouiseBevilacqua, AntonioKechadi, TaharCaulfield, Brian
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Date: 1-Jan-2020
Online since: 2020-10-20T11:25:14Z
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.
Funding Details: 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: SegmentationHome rehabilitationInertial measurement unitMachine learningShoulder
DOI: 10.1177/2055668320915377
Language: en
Status of Item: Peer reviewed
ISSN: 2055-6683
Appears in Collections:Computer Science Research Collection
Public Health, Physiotherapy and Sports Science Research Collection
Insight Research Collection

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