Ecological validity of a deep learning algorithm to detect gait events from real-life walking bouts in mobility-limiting diseases

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,...

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Authors: Romijnders, Robbin, Carsin, Anne-Elie, García Aymerich, Judith, Koch, Sarah, Maetzler, Walter
Format: article
Status:Published version
Publication Date:2023
Country:España
Institution:Universitat Pompeu Fabra
Repository:Repositorio Digital de la UPF
OAI Identifier:oai:repositori.upf.edu:10230/59121
Online Access:http://hdl.handle.net/10230/59121
http://dx.doi.org/10.3389/fneur.2023.1247532
Access Level:Open access
Keyword:Deep learning (artificial intelligence)
Free-living
Gait analysis
Gait events detection
Inertial measurement unit (IMU)
Mobility
id ES_c4890b04d2b403aa263d9b2d93de29f5
oai_identifier_str oai:repositori.upf.edu:10230/59121
network_acronym_str ES
network_name_str España
repository_id_str
dc.title.none.fl_str_mv Ecological validity of a deep learning algorithm to detect gait events from real-life walking bouts in mobility-limiting diseases
title Ecological validity of a deep learning algorithm to detect gait events from real-life walking bouts in mobility-limiting diseases
spellingShingle Ecological validity of a deep learning algorithm to detect gait events from real-life walking bouts in mobility-limiting diseases
Romijnders, Robbin
Deep learning (artificial intelligence)
Free-living
Gait analysis
Gait events detection
Inertial measurement unit (IMU)
Mobility
title_short Ecological validity of a deep learning algorithm to detect gait events from real-life walking bouts in mobility-limiting diseases
title_full Ecological validity of a deep learning algorithm to detect gait events from real-life walking bouts in mobility-limiting diseases
title_fullStr Ecological validity of a deep learning algorithm to detect gait events from real-life walking bouts in mobility-limiting diseases
title_full_unstemmed Ecological validity of a deep learning algorithm to detect gait events from real-life walking bouts in mobility-limiting diseases
title_sort Ecological validity of a deep learning algorithm to detect gait events from real-life walking bouts in mobility-limiting diseases
dc.creator.none.fl_str_mv Romijnders, Robbin
Carsin, Anne-Elie
García Aymerich, Judith
Koch, Sarah
Maetzler, Walter
author Romijnders, Robbin
author_facet Romijnders, Robbin
Carsin, Anne-Elie
García Aymerich, Judith
Koch, Sarah
Maetzler, Walter
author_role author
author2 Carsin, Anne-Elie
García Aymerich, Judith
Koch, Sarah
Maetzler, Walter
author2_role author
author
author
author
dc.subject.none.fl_str_mv Deep learning (artificial intelligence)
Free-living
Gait analysis
Gait events detection
Inertial measurement unit (IMU)
Mobility
topic Deep learning (artificial intelligence)
Free-living
Gait analysis
Gait events detection
Inertial measurement unit (IMU)
Mobility
description 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.
publishDate 2023
dc.date.none.fl_str_mv 2023
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10230/59121
http://dx.doi.org/10.3389/fneur.2023.1247532
url http://hdl.handle.net/10230/59121
http://dx.doi.org/10.3389/fneur.2023.1247532
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Front Neurol. 2023 Oct 16;14:1247532
info:eu-repo/grantAgreement/EC/H2020/820820
dc.rights.none.fl_str_mv http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Frontiers
publisher.none.fl_str_mv Frontiers
dc.source.none.fl_str_mv reponame:Repositorio Digital de la UPF
instname:Universitat Pompeu Fabra
instname_str Universitat Pompeu Fabra
reponame_str Repositorio Digital de la UPF
collection Repositorio Digital de la UPF
repository.name.fl_str_mv
repository.mail.fl_str_mv
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spelling Ecological validity of a deep learning algorithm to detect gait events from real-life walking bouts in mobility-limiting diseasesRomijnders, RobbinCarsin, Anne-ElieGarcía Aymerich, JudithKoch, SarahMaetzler, WalterDeep learning (artificial intelligence)Free-livingGait analysisGait events detectionInertial measurement unit (IMU)MobilityIntroduction: 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.This study was supported by the Mobilise-D project that has received funding from the Innovative Medicines Initiative 2 Joint Undertaking (JU) under grant agreement No. 820820. This JU receives support from the European Union's Horizon 2020 research and innovation program and the European Federation of Pharmaceutical Industries and Associations (EFPIA). LA, LR, AY, and SD were also supported by the National Institute for Health Research (NIHR) Newcastle Biomedical Research Centre (BRC) based at Newcastle upon Tyne Hospital NHS Foundation Trust and Newcastle University and the NIHR/Wellcome Trust Clinical Research Facility (CRF) infrastructure at Newcastle upon the Tyne Hospitals NHS Foundation Trust. This study was supported by the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG) through the project B9 of the Collaborative Research Centre CRC 1261 Magnetoelectric Sensors: From Composite Materials to Biomagnetic Diagnostics.Frontiers202420242023info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/10230/59121http://dx.doi.org/10.3389/fneur.2023.1247532reponame:Repositorio Digital de la UPFinstname:Universitat Pompeu FabraInglésFront Neurol. 2023 Oct 16;14:1247532info:eu-repo/grantAgreement/EC/H2020/820820© 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) (http://creativecommons.org/licenses/by/4.0/). 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.http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:repositori.upf.edu:10230/591212026-06-12T07:21:37Z
score 15,812429

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