Real-World gait detection using a wrist-worn inertial sensor: Validation study

Mostra el registre complet Registre parcial de l'ítem

• dc.contributor.author Kluge, Felix
• dc.contributor.author Buekers, Joren
• dc.contributor.author Carsin, Anne-Elie
• dc.contributor.author García Aymerich, Judith
• dc.contributor.author Koch, Sarah
• dc.contributor.author Mueller, Arne
• dc.date.accessioned 2024-06-12T06:17:10Z
• dc.date.available 2024-06-12T06:17:10Z
• dc.date.issued 2024
• dc.description.abstract Background: Wrist-worn inertial sensors are used in digital health for evaluating mobility in real-world environments. Preceding the estimation of spatiotemporal gait parameters within long-term recordings, gait detection is an important step to identify regions of interest where gait occurs, which requires robust algorithms due to the complexity of arm movements. While algorithms exist for other sensor positions, a comparative validation of algorithms applied to the wrist position on real-world data sets across different disease populations is missing. Furthermore, gait detection performance differences between the wrist and lower back position have not yet been explored but could yield valuable information regarding sensor position choice in clinical studies. Objective: The aim of this study was to validate gait sequence (GS) detection algorithms developed for the wrist position against reference data acquired in a real-world context. In addition, this study aimed to compare the performance of algorithms applied to the wrist position to those applied to lower back-worn inertial sensors. Methods: Participants with Parkinson disease, multiple sclerosis, proximal femoral fracture (hip fracture recovery), chronic obstructive pulmonary disease, and congestive heart failure and healthy older adults (N=83) were monitored for 2.5 hours in the real-world using inertial sensors on the wrist, lower back, and feet including pressure insoles and infrared distance sensors as reference. In total, 10 algorithms for wrist-based gait detection were validated against a multisensor reference system and compared to gait detection performance using lower back-worn inertial sensors. Results: The best-performing GS detection algorithm for the wrist showed a mean (per disease group) sensitivity ranging between 0.55 (SD 0.29) and 0.81 (SD 0.09) and a mean (per disease group) specificity ranging between 0.95 (SD 0.06) and 0.98 (SD 0.02). The mean relative absolute error of estimated walking time ranged between 8.9% (SD 7.1%) and 32.7% (SD 19.2%) per disease group for this algorithm as compared to the reference system. Gait detection performance from the best algorithm applied to the wrist inertial sensors was lower than for the best algorithms applied to the lower back, which yielded mean sensitivity between 0.71 (SD 0.12) and 0.91 (SD 0.04), mean specificity between 0.96 (SD 0.03) and 0.99 (SD 0.01), and a mean relative absolute error of estimated walking time between 6.3% (SD 5.4%) and 23.5% (SD 13%). Performance was lower in disease groups with major gait impairments (eg, patients recovering from hip fracture) and for patients using bilateral walking aids. Conclusions: Algorithms applied to the wrist position can detect GSs with high performance in real-world environments. Those periods of interest in real-world recordings can facilitate gait parameter extraction and allow the quantification of gait duration distribution in everyday life. Our findings allow taking informed decisions on alternative positions for gait recording in clinical studies and public health. Trial registration: ISRCTN Registry 12246987; https://www.isrctn.com/ISRCTN12246987. International registered report identifier (irrid): RR2-10.1136/bmjopen-2021-050785.
• dc.description.sponsorship This work was supported by the Mobilise-D project that has received funding from the Innovative Medicines Initiative 2 Joint Undertaking (IMI2 JU; grant 820820). The IMI2 JU receives support from the European Union’s Horizon 2020 research and innovation program and the European Federation of Pharmaceutical Industries and Associations. SDD, LR, and AY were also supported by the IMI2 JU project Identifying Digital Endpoints to Assess Fatigue, Sleep and Activities in Daily Living (grant 853981). LA, LR, AY, and SDD were also supported by the National Institute for Health Research (NIHR) Newcastle Biomedical Research Centre (BRC) based at The Newcastle upon Tyne Hospital NHS Foundation Trust, Newcastle University and the Cumbria, Northumberland and Tyne and Wear NHS Foundation Trust. JMH and YEB are supported in part by the National Institutes of Health (grant R01AG79133). LA, LR, AY, and SDD were also supported by the NIHR/Wellcome Trust Clinical Research Facility infrastructure at The Newcastle upon Tyne Hospitals NHS Foundation Trust. This study was also supported by the NIHR through the Sheffield BRC (grant IS-BRC-1215-20017). ISGlobal acknowledges support from the Spanish Ministry of Science and Innovation through the “Centro de Excelencia Severo Ochoa 2019-2023” program (CEX2018-000806-S) and from the Generalitat de Catalunya through the Centres de Recerca de Catalunya program. All opinions are those of the authors and not the funders. The content in this publication reflects the authors’ view, and neither Innovative Medicines Initiative nor the European Union, European Federation of Pharmaceutical Industries and Associations, National Health Service, NIHR, Department of Health and Social Care, or any associated partners are responsible for any use that may be made of the information contained herein.
• dc.format.mimetype application/pdf
• dc.identifier.citation Kluge F, Brand YE, Micó-Amigo ME, Bertuletti S, D'Ascanio I, Gazit E, Bonci T, et al. Real-World gait detection using a wrist-worn inertial sensor: Validation study. JMIR Form Res. 2024 May 1;8:e50035. DOI: 10.2196/50035
• dc.identifier.doi http://dx.doi.org/10.2196/50035
• dc.identifier.issn 2561-326X
• dc.identifier.uri http://hdl.handle.net/10230/60440
• dc.language.iso eng
• dc.publisher JMIR Publications
• dc.relation.ispartof JMIR Form Res. 2024 May 1;8:e50035
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/820820
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/853981
• dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/CEX2018-000806-S
• dc.rights This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Formative Research, is properly cited. The complete bibliographic information, a link to the original publication on https://formative.jmir.org, as well as this copyright and license information must be included.
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by/4.0/
• dc.subject.keyword Mobilise-D
• dc.subject.keyword Accelerometer
• dc.subject.keyword Digital health
• dc.subject.keyword Digital mobility outcomes
• dc.subject.keyword Inertial measurement unit
• dc.subject.keyword Validation
• dc.subject.keyword Walking
• dc.subject.keyword Wearable sensor
• dc.title Real-World gait detection using a wrist-worn inertial sensor: Validation study
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion