Inertial Sensor Estimation of Initial and Terminal Contact during In-Field Running.

acceleration angular velocity gait analysis inertial measurement device

Journal

Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366

Informations de publication

Date de publication:
25 Jun 2022
Historique:
received: 27 05 2022
revised: 17 06 2022
accepted: 24 06 2022
entrez: 9 7 2022
pubmed: 10 7 2022
medline: 14 7 2022
Statut: epublish

Résumé

Given the popularity of running-based sports and the rapid development of Micro-electromechanical systems (MEMS), portable wireless sensors can provide in-field monitoring and analysis of running gait parameters during exercise. This paper proposed an intelligent analysis system from wireless micro-Inertial Measurement Unit (IMU) data to estimate contact time (CT) and flight time (FT) during running based on gyroscope and accelerometer sensors in a single location (ankle). Furthermore, a pre-processing system that detected the running period was introduced to analyse and enhance CT and FT detection accuracy and reduce noise. Results showed pre-processing successfully detected the designated running periods to remove noise of non-running periods. Furthermore, accelerometer and gyroscope algorithms showed good consistency within 95% confidence interval, and average absolute error of 31.53 ms and 24.77 ms, respectively. In turn, the combined system obtained a consistency of 84-100% agreement within tolerance values of 50 ms and 30 ms, respectively. Interestingly, both accuracy and consistency showed a decreasing trend as speed increased (36% at high-speed fore-foot strike). Successful CT and FT detection and output validation with consistency checking algorithms make in-field measurement of running gait possible using ankle-worn IMU sensors. Accordingly, accurate IMU-based gait analysis from gyroscope and accelerometer information can inform future research on in-field gait analysis.

Identifiants

pubmed: 35808307
pii: s22134812
doi: 10.3390/s22134812
pmc: PMC9269345
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

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Auteurs

Yue Yang (Y)

Faculty of Engineering and IT, University of Technology Sydney, 81 Broadway, Ultimo, NSW 2007, Australia.

Li Wang (L)

Faculty of Engineering and IT, University of Technology Sydney, 81 Broadway, Ultimo, NSW 2007, Australia.

Steven Su (S)

Faculty of Engineering and IT, University of Technology Sydney, 81 Broadway, Ultimo, NSW 2007, Australia.

Mark Watsford (M)

School of Sport, Exercise and Rehabilitation, Faculty of Health, University of Technology Sydney, Moore Park, NSW 2007, Australia.

Lauren Marie Wood (LM)

Graduate School of Biomedical Engineering, Samuels Building (F25) Library Rd, UNSW, Kensington, NSW 2052, Australia.

Rob Duffield (R)

School of Sport, Exercise and Rehabilitation, Faculty of Health, University of Technology Sydney, Moore Park, NSW 2007, Australia.

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Classifications MeSH