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Article

An Automatic Gait Analysis Pipeline for Wearable Sensors: A Pilot Study in Parkinson’s Disease

1
IXICO, London EC1A 9PN, UK
2
Department of Human Centred Computing, Monash University, Clayton, VIC 3800, Australia
3
Electrical and Electronic Engineering, School of Computer Science, University of Bristol, Bristol BS8 1QU, UK
4
Translational Health Sciences, University of Bristol Medical School, Bristol BS8 1QU, UK
5
Movement Disorders Group, North Bristol NHS Trust, Westbury on Trym, Bristol BS10 5NB, UK
6
Department of Computing, Imperial College London, London SW7 2AZ, UK
*
Author to whom correspondence should be addressed.
Academic Editors: Barry Greene, Rahul Soangra and Alan Bourke
Sensors 2021, 21(24), 8286; https://doi.org/10.3390/s21248286
Received: 16 October 2021 / Revised: 6 December 2021 / Accepted: 8 December 2021 / Published: 11 December 2021
(This article belongs to the Special Issue Wearable Sensors for Assessment of Gait in Older Adults)
The use of wearable sensors allows continuous recordings of physical activity from participants in free-living or at-home clinical studies. The large amount of data collected demands automatic analysis pipelines to extract gait parameters that can be used as clinical endpoints. We introduce a deep learning-based automatic pipeline for wearables that processes tri-axial accelerometry data and extracts gait events—bout segmentation, initial contact (IC), and final contact (FC)—from a single sensor located at either the lower back (near L5), shin or wrist. The gait events detected are posteriorly used for gait parameter estimation, such as step time, length, and symmetry. We report results from a leave-one-subject-out (LOSO) validation on a pilot study dataset of five participants clinically diagnosed with Parkinson’s disease (PD) and six healthy controls (HC). Participants wore sensors at three body locations and walked on a pressure-sensing walkway to obtain reference gait data. Mean absolute errors (MAE) for the IC events ranged from 22.82 to 33.09 milliseconds (msecs) for the lower back sensor while for the shin and wrist sensors, MAE ranges were 28.56–64.66 and 40.19–72.50 msecs, respectively. For the FC-event detection, MAE ranges were 29.06–48.42, 40.19–72.70 and 36.06–60.18 msecs for the lumbar, wrist and shin sensors, respectively. Intraclass correlation coefficients, ICC(2,k), between the estimated parameters and the reference data resulted in good-to-excellent agreement (ICC ≥ 0.84) for the lumbar and shin sensors, excluding the double support time (ICC = 0.37 lumbar and 0.38 shin) and swing time (ICC = 0.55 lumbar and 0.59 shin). The wrist sensor also showed good agreements, but the ICCs were lower overall than for the other two sensors. Our proposed analysis pipeline has the potential to extract up to 100 gait-related parameters, and we expect our contribution will further support developments in the fields of wearable sensors, digital health, and remote monitoring in clinical trials. View Full-Text
Keywords: accelerometry; deep learning; free living; step length; initial contact; toe-off accelerometry; deep learning; free living; step length; initial contact; toe-off
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MDPI and ACS Style

Peraza, L.R.; Kinnunen, K.M.; McNaney, R.; Craddock, I.J.; Whone, A.L.; Morgan, C.; Joules, R.; Wolz, R. An Automatic Gait Analysis Pipeline for Wearable Sensors: A Pilot Study in Parkinson’s Disease. Sensors 2021, 21, 8286. https://doi.org/10.3390/s21248286

AMA Style

Peraza LR, Kinnunen KM, McNaney R, Craddock IJ, Whone AL, Morgan C, Joules R, Wolz R. An Automatic Gait Analysis Pipeline for Wearable Sensors: A Pilot Study in Parkinson’s Disease. Sensors. 2021; 21(24):8286. https://doi.org/10.3390/s21248286

Chicago/Turabian Style

Peraza, Luis R., Kirsi M. Kinnunen, Roisin McNaney, Ian J. Craddock, Alan L. Whone, Catherine Morgan, Richard Joules, and Robin Wolz. 2021. "An Automatic Gait Analysis Pipeline for Wearable Sensors: A Pilot Study in Parkinson’s Disease" Sensors 21, no. 24: 8286. https://doi.org/10.3390/s21248286

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