Welcome to the UPF Digital Repository

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

Show simple item record

dc.contributor.author Romijnders, Robbin
dc.contributor.author Carsin, Anne-Elie
dc.contributor.author García Aymerich, Judith
dc.contributor.author Koch, Sarah
dc.contributor.author Maetzler, Walter
dc.date.accessioned 2024-02-19T07:19:35Z
dc.date.available 2024-02-19T07:19:35Z
dc.date.issued 2023
dc.identifier.citation Romijnders R, Salis F, Hansen C, Küderle A, Paraschiv-Ionescu A, Cereatti A et al. Ecological validity of a deep learning algorithm to detect gait events from real-life walking bouts in mobility-limiting diseases. Front Neurol. 2023 Oct 16;14:1247532. DOI: 10.3389/fneur.2023.1247532
dc.identifier.issn 1664-2295
dc.identifier.uri http://hdl.handle.net/10230/59121
dc.description.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.
dc.description.sponsorship 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.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Frontiers
dc.relation.ispartof Front Neurol. 2023 Oct 16;14:1247532
dc.rights © 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.
dc.rights.uri http://creativecommons.org/licenses/by/4.0/
dc.title Ecological validity of a deep learning algorithm to detect gait events from real-life walking bouts in mobility-limiting diseases
dc.type info:eu-repo/semantics/article
dc.identifier.doi http://dx.doi.org/10.3389/fneur.2023.1247532
dc.subject.keyword Deep learning (artificial intelligence)
dc.subject.keyword Free-living
dc.subject.keyword Gait analysis
dc.subject.keyword Gait events detection
dc.subject.keyword Inertial measurement unit (IMU)
dc.subject.keyword Mobility
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/820820
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion

Thumbnail

This item appears in the following Collection(s)

Show simple item record

Search DSpace


Advanced Search

Browse

My Account

Statistics

In collaboration with Compliant to Partaking