Article
Data-Driven Investigation of Gait Patterns in Individuals
Affected by Normal Pressure Hydrocephalus
Kiran Kuruvithadam
1
, Marcel Menner
2
, William R. Taylor
3
, Melanie N. Zeilinger
2
, Lennart Stieglitz
4,†
and Marianne Schmid Daners
1,
*
,†
Citation: Kuruvithadam, K.; Menner,
M.; Taylor, W.R.; Zeilinger, M.N.;
Stieglitz, L.; Schmid Daners, M.
Data-Driven Investigation of Gait
Patterns in Individuals Affected by
Normal Pressure Hydrocephalus.
Sensors 2021, 21, 6451. https://
doi.org/10.3390/s21196451
Academic Editors: Subhas
Mukhopadhyay, Nunzio Cennamo,
M. Jamal Deen, Junseop Lee and
imone Morais
Received: 17 August 2021
Accepted: 15 September 2021
Published: 27 September 2021
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims in
published maps and institutional affil-
iations.
Copyright: © 2021 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
1
Product Development Group Zurich, Department of Mechanical and Process Engineering, ETH Zurich,
8092 Zurich, Switzerland; kiranku@ethz.ch
2
Institute for Dynamic Systems and Control, ETH Zurich,
8092 Zurich, Switzerland;
menner@ieee.org (M.M.);
mzeilinger@ethz.ch (M.N.Z.)
3
Laboratory for Movement Biomechanics, Institute for Biomechanics, ETH Zurich, 8093 Zurich, Switzerland;
bt@ethz.ch
4
Department of Neurosurgery, University Hospital Zurich,
8091 Zurich, Switzerland;
lennart.stieglitz@usz.ch
* Correspondence: marischm@ethz.ch
† These authors share senior authorship.
Abstract:
Normal pressure hydrocephalus (NPH) is a chronic and progressive disease that affects
predominantly elderly subjects. The most prevalent symptoms are gait disorders, generally deter-
mined by visual observation or measurements taken in complex laboratory environments. However,
controlled testing environments can have a significant influence on the way subjects walk and hinder
the identification of natural walking characteristics. The study aimed to investigate the differences in
walking patterns between a controlled environment (10 m walking test) and real-world environment
(72 h recording) based on measurements taken via a wearable gait assessment device. We tested
whether real-world environment measurements can be beneficial for the identification of gait disor-
ders by performing a comparison of patients’ gait parameters with an aged-matched control group
in both environments. Subsequently, we implemented four machine learning classifiers to inspect the
individual strides’ profiles. Our results on twenty young subjects, twenty elderly subjects and twelve
NPH patients indicate that patients exhibited a considerable difference between the two environ-
ments, in particular gait speed (p-value
p =
0.0073), stride length (p-value
p =
0.0073), foot clearance
(p-value
p =
0.0117) and swing/stance ratio (p-value
p =
0.0098). Importantly, measurements taken
in real-world environments yield a better discrimination of NPH patients compared to the controlled
setting. Finally, the use of stride classifiers provides promise in the identification of strides affected
by motion disorders.
Keywords:
hydrocephalus; gait analysis; kinematic measurement; machine learning; neural network;
regression analysis; wearable sensors
1. Introduction
Gait patterns can provide a good insight into the overall health status of a subject,
since walking involves well-coordinated participation of different body systems [
1
]. Cur-
rent methods rely on the evaluation of walking characteristics in controlled laboratory
environments, which are likely to falter the observations because of psychological pressure
and resulting bias [
2
,
3
]. Here, in-depth studies can support our understanding of how the
environment can affect measurements of subjects of different age groups and health status,
as it was the case in a previous pilot study by Renggli et al. [
4
], which has demonstrated
how a controlled lab setting is able to affect healthy elderly subjects, resulting in a signifi-
cant deviation from their natural gait patterns. By virtue of these observations, the current
study hypothesizes that such deviations in gait characteristics between real-world and con-
trolled lab environments could also be expected for subjects that suffer from neurological
Sensors 2021, 21, 6451. https://doi.org/10.3390/s21196451 https://www.mdpi.com/journal/sensors
