University of Groningen
Machine learning for identifying patterns in human gait
Zhou, Yuhan
DOI:
10.33612/diss.159240405
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Publication date:
2021
Link to publication in University of Groningen/UMCG research database
Citation for published version (APA):
Zhou, Y. (2021). Machine learning for identifying patterns in human gait: Classification of age and clinical
groups. University of Groningen. https://doi.org/10.33612/diss.159240405
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Machine learning for identifying
patterns in human gait
Classification of age and clinical groups
M
achine l
earnin
g f
or i
de
ntif
yin
g p
att
erns in h
uma
n g
ait
YUH
AN
ZH
O
U
YUHAN ZHOU
INVITATION
for attending the public
defense of the thesis entitled
Machine learning for
identifying patterns
in human gait
Classification of age
and clinical groups
by
Yuhan Zhou
y.zhou01@umcg.nl
On Wednesday 17 March 2021,
at 12.45 p.m.
In the Academy Building of
the University of Groningen.
Broerstraat 5, Groningen
YUHAN ZHOU
Paranymphs
Xiaoping Zheng
x.zheng@umcg.nl
Lisanne Bakker
l.b.m.bakker@umcg.nl
The studies described in chapters 2, 3 and 4 were conducted at the Department of Human Movement Sciences, University Medical Center Groningen, Groningen, the Netherlands. The study described in chapter 5 was conducted at the Department of Human Movement Sciences, University Medical Center Groningen, Groningen, the Netherlands and the Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, United Kindom.
This study was financially supported by the Keep Control program, which received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 721577.
PhD training was facilitated by Research Institute School of Health Research (SHARE), part of the Graduate School of Medical Science Groningen.
The printing of this thesis was financially supported by the University of Groningen, University Medical Center Groningen and Research Institute SHARE.
Paranymphs: Xiaoping Zheng Lisanne Bakker
Cover image: Uthai pr, shutterstock.com
Layout and design: Daniëlle Balk, persoonlijkproefschrift.nl Printing: Gildeprint Enschede, gildeprint.nl
ISBN: 9789464191431
Machine learning for identifying patterns in human gait: Classification of age and clinical groups Yuhan Zhou
Copyright © 2021 Yuhan Zhou
All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic and mechanical, including photocopying, recording or any information storage or retrieval system, without written permission from the author.
3
Machine learning for identifying
patterns in human gait
Classification of age and clinical groups
PhD thesis
to obtain the degree of PhD at the University of Groningen
on the authority of the Rector Magnificus Prof. C. Wijmenga
and in accordance with the decision by the College of Deans. This thesis will be defended in public on Wednesday 17 March 2021 at 12.45 hours
by
Yuhan Zhou
born on 18 January 1990 in Jiangsu, China
4
Supervisors
Prof. C.J.C. Lamoth Prof. T. HortobágyiAssessment Committee
Prof. A. Daffertshofer Prof. N.M. Maurits Prof. E. OttenTABLE OF CONTENTS
Chapter 1 General introduction 7
Chapter 2 The detection of age groups by dynamic gait outcomes using machine
learning approaches 21
Zhou, Y., Romijnders, R., Hansen, C., van Campen, J., Maetzler, W., Hortobágyi, T., Lamoth, C.J.C. (2020). Scientific reports, 10(1), 1-12.
Chapter 3 Artificial neural network accurately classified cognitively impaired
and intact geriatric patients using a combination of gait and clinical characteristics
45 Zhou, Y., van Campen, J., Hortobágyi, T., Lamoth, C.J.C., submitted for publication
Chapter 4 Classification of neurological patients to identify fallers based on
spatial-temporal gait characteristics measured by a wearable device 65
Zhou, Y., Zia Ur Rehman, R., Hansen, C., Maetzler, W., Del Din, S., Rochester, L., Hortobágyi, T. Lamoth, C.J.C. Sensors, 2020; 20(15):4098.
Chapter 5 Gait analysis with wearables can accurately classify fallers from
non-fallers: a step toward better management of neurological disorders 87
Zia Ur Rehman, R., Zhou, Y., Del Din, S., Alcock, L., Hansen, C., Guan, Y., Hortobágyi, T., Maetzler, W., Rochester, L., Lamoth, C.J.C. Sensors. 2020; 20(23):6992. (equal first authors)
Chapter 6 General discussion 113
Appendix A Summary 128
Samenvatting 131
概要 134
Acknowledgements 136
About the author 139
