Classification of movement of people with parkinsons disease using wearable inertial movement units and machine learning

Ireland, David, Wang, Ziwei, Lamont, Robyn and Liddle, Jacki (2016). Classification of movement of people with parkinsons disease using wearable inertial movement units and machine learning. In: Andrew Georgiou, Louise K. Schaper and Sue Whetton, Digital Health Innovation for Consumers, Clinicians, Connectivity and Community - Selected Papers from the 24th Australian National Health Informatics Conference, HIC 2016. 24th Australian National Health Informatics Conference, HIC 2016, Melbourne, Australia, (61-66). 25 - 27 July 2016. doi:10.3233/978-1-61499-666-8-61


Author Ireland, David
Wang, Ziwei
Lamont, Robyn
Liddle, Jacki
Title of paper Classification of movement of people with parkinsons disease using wearable inertial movement units and machine learning
Conference name 24th Australian National Health Informatics Conference, HIC 2016
Conference location Melbourne, Australia
Conference dates 25 - 27 July 2016
Proceedings title Digital Health Innovation for Consumers, Clinicians, Connectivity and Community - Selected Papers from the 24th Australian National Health Informatics Conference, HIC 2016   Check publisher's open access policy
Journal name Studies in Health Technology and Informatics   Check publisher's open access policy
Series Studies in Health Technology and Informatics
Place of Publication Amsterdam, Netherlands
Publisher I O S Press
Publication Year 2016
Sub-type Fully published paper
DOI 10.3233/978-1-61499-666-8-61
Open Access Status DOI
ISBN 9781614996651
9781614996668
1614996660
ISSN 1879-8365
0926-9630
Editor Andrew Georgiou
Louise K. Schaper
Sue Whetton
Volume 227
Start page 61
End page 66
Total pages 6
Language eng
Abstract/Summary In this work, inertial movement units were placed on people with Parkinsons disease (PwPD) who subsequently performed a standard test of walking endurance (six-minute walk test-6MWT). Five devices were placed on each the limbs and small of the back. These devices captured the acceleration and rotational motion while the person walked as far as they can in six minutes. The wearable devices can objectively indicate the pattern and rhythmicity of limb and body movements. It is possible that this data, when subject to machine learning could provide additional objective measures that may support clinical observations related to the quality of movement. The aim of this work is two fold. First, to identify the most useful features of the captured signals; second, to identify the accuracy of using these features to predict the severity of PD as measured by standard clinical assessment.
Keyword Machine learning
Neurodegenerative diseases
Remote monitoring
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status UQ

 
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Created: Fri, 25 Nov 2016, 20:48:27 EST by Kirstie Asmussen on behalf of Learning and Research Services (UQ Library)