Original article: Validity and reliability of gait metrics derived from researcher-placed and self-placed wearable inertial sensors.

Biomechanics Inertial sensors Reliability Remote collections Validity

Journal

Journal of biomechanics
ISSN: 1873-2380
Titre abrégé: J Biomech
Pays: United States
ID NLM: 0157375

Informations de publication

Date de publication:
09 2022
Historique:
received: 18 02 2022
revised: 08 08 2022
accepted: 12 08 2022
pubmed: 29 8 2022
medline: 9 9 2022
entrez: 28 8 2022
Statut: ppublish

Résumé

To compare the inter-session placement reliability for researcher-placed and self-placed sensors, and to evaluate the validity and reliability of waveforms and discrete variables from researcher-placed and self-placed sensors following a previously described alignment correction algorithm. Fourteen healthy, pain-free participants underwent gait analysis over two data collection sessions. Participants self-placed an inertial sensor on their left tibia and a researcher placed one on their right tibia, before completing 10 overground walking trials. Following an axis correction from a principal component analysis-based algorithm, validity and reliability were assessed within and between days for each sensor placement type through Euclidean distances, waveforms, and discrete outcomes. The placement location of researcher-placed sensors exhibited good inter-session reliability (ICC = 0.85) in comparison to self-placed sensors (ICC = 0.55). Similarly, waveforms from researcher-placed sensors exhibited excellent validity across all variables (CMC ≥ 0.90), while self-placed sensors saw high validity for most axes with reductions in validity for mediolateral acceleration and frontal plane angular velocity. Discrete outcomes saw good to excellent reliability across both sensor placement types. A simple alignment correction algorithm for inertial sensor gait data demonstrated good to excellent validity and reliability in self-placed sensors with no additional data or measures. This method can be used to align sensors easily and effectively despite sensor placement errors during straight, level walking to improve 3D gait data outcomes in data collected with self-placed sensors.

Identifiants

pubmed: 36030636
pii: S0021-9290(22)00304-9
doi: 10.1016/j.jbiomech.2022.111263
pii:
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

111263

Subventions

Organisme : CIHR
Pays : Canada

Informations de copyright

Copyright © 2022. Published by Elsevier Ltd.

Déclaration de conflit d'intérêts

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Auteurs

Matthew C Ruder (MC)

Department of Kinesiology, McMaster University, Hamilton, ON, Canada. Electronic address: ruderm@mcmaster.ca.

Michael A Hunt (MA)

Motion Analysis and Biofeedback Laboratory, University of British Columbia, Vancouver, BC, Canada; Department of Physical Therapy, University of British Columbia, Vancouver, BC, Canada.

Jesse M Charlton (JM)

Motion Analysis and Biofeedback Laboratory, University of British Columbia, Vancouver, BC, Canada; Graduate Programs in Rehabilitation Sciences, University of British Columbia, Vancouver, BC, Canada.

Calvin T F Tse (CTF)

Motion Analysis and Biofeedback Laboratory, University of British Columbia, Vancouver, BC, Canada; Graduate Programs in Rehabilitation Sciences, University of British Columbia, Vancouver, BC, Canada.

Dylan Kobsar (D)

Department of Kinesiology, McMaster University, Hamilton, ON, Canada.

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