Romijnders, R., Salis, F., Hansen, C. et al. (46 more authors) (2023) Ecological validity of a deep learning algorithm to detect gait events from real-life walking bouts in mobility-limiting diseases. Frontiers in Neurology, 14. 1247532. ISSN 1664-2295
Abstract
Introduction: The clinical assessment of mobility, and walking specifically, is still mainly based on functional tests that lack ecological validity. Thanks to inertial measurement units (IMUs), gait analysis is shifting to unsupervised monitoring in naturalistic and unconstrained settings. However, the extraction of clinically relevant gait parameters from IMU data often depends on heuristics-based algorithms that rely on empirically determined thresholds. These were mainly validated on small cohorts in supervised settings.
Methods: Here, a deep learning (DL) algorithm was developed and validated for gait event detection in a heterogeneous population of different mobility-limiting disease cohorts and a cohort of healthy adults. Participants wore pressure insoles and IMUs on both feet for 2.5 h in their habitual environment. The raw accelerometer and gyroscope data from both feet were used as input to a deep convolutional neural network, while reference timings for gait events were based on the combined IMU and pressure insoles data.
Results and discussion: The results showed a high-detection performance for initial contacts (ICs) (recall: 98%, precision: 96%) and final contacts (FCs) (recall: 99%, precision: 94%) and a maximum median time error of −0.02 s for ICs and 0.03 s for FCs. Subsequently derived temporal gait parameters were in good agreement with a pressure insoles-based reference with a maximum mean difference of 0.07, −0.07, and <0.01 s for stance, swing, and stride time, respectively. Thus, the DL algorithm is considered successful in detecting gait events in ecologically valid environments across different mobility-limiting diseases.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2023 Romijnders, Salis, Hansen, Küderle, Paraschiv-Ionescu, Cereatti, Alcock, Aminian, Becker, Bertuletti, Bonci, Brown, Buckley, Cantu, Carsin, Caruso, Caulfield, Chiari, D'Ascanio, Del Din, Eskofier, Fernstad, Fröhlich, Garcia Aymerich, Gazit, Hausdorff, Hiden, Hume, Keogh, Kirk, Kluge, Koch, Mazzà, Megaritis, Micó-Amigo, Müller, Palmerini, Rochester, Schwickert, Scott, Sharrack, Singleton, Soltani, Ullrich, Vereijken, Vogiatzis, Yarnall, Schmidt and Maetzler. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
Keywords: | deep learning (artificial intelligence); free-living; gait analysis; gait events detection; inertial measurement unit (IMU); mobility |
Dates: |
|
Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Mechanical Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 06 Nov 2023 10:38 |
Last Modified: | 06 Nov 2023 10:38 |
Status: | Published |
Publisher: | Frontiers Media SA |
Refereed: | Yes |
Identification Number: | 10.3389/fneur.2023.1247532 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:204945 |