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

Yang, Y; Wang, L; Su, S; Watsford, M; Wood, LM; Duffield, R

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

Yang, Y Wang, L Su, S

Watsford, M

Wood, LM Duffield, R

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Publisher:: MDPI AG
Publication Type:: Journal Article
Citation:: Sensors (Basel), 2022, 22, (13), pp. 1-13
Issue Date:: 2022-06-25

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Field	Value	Language
dc.contributor.author	Yang, Y
dc.contributor.author	Wang, L
dc.contributor.author	Su, S https://orcid.org/0000-0002-5720-8852
dc.contributor.author	Watsford, M https://orcid.org/0000-0002-4569-6377
dc.contributor.author	Wood, LM
dc.contributor.author	Duffield, R https://orcid.org/0000-0002-5641-1314
dc.date.accessioned	2022-10-04T04:12:18Z
dc.date.available	2022-06-24
dc.date.available	2022-10-04T04:12:18Z
dc.date.issued	2022-06-25
dc.identifier.citation	Sensors (Basel), 2022, 22, (13), pp. 1-13
dc.identifier.issn	1424-8220
dc.identifier.issn	1424-8220
dc.identifier.uri	http://hdl.handle.net/10453/162290
dc.description.abstract	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.
dc.format	Electronic
dc.language	eng
dc.publisher	MDPI AG
dc.relation.ispartof	Sensors (Basel)
dc.relation.isbasedon	10.3390/s22134812
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0301 Analytical Chemistry, 0502 Environmental Science and Management, 0602 Ecology, 0805 Distributed Computing, 0906 Electrical and Electronic Engineering
dc.subject.classification	Analytical Chemistry
dc.subject.mesh	Algorithms
dc.subject.mesh	Biomechanical Phenomena
dc.subject.mesh	Foot
dc.subject.mesh	Gait
dc.subject.mesh	Gait Analysis
dc.subject.mesh	Algorithms
dc.subject.mesh	Biomechanical Phenomena
dc.subject.mesh	Foot
dc.subject.mesh	Gait
dc.subject.mesh	Gait Analysis
dc.subject.mesh	Foot
dc.subject.mesh	Gait
dc.subject.mesh	Algorithms
dc.subject.mesh	Biomechanical Phenomena
dc.subject.mesh	Gait Analysis
dc.title	Inertial Sensor Estimation of Initial and Terminal Contact during In-Field Running.
dc.type	Journal Article
utslib.citation.volume	22
utslib.location.activity	Switzerland
utslib.for	0301 Analytical Chemistry
utslib.for	0502 Environmental Science and Management
utslib.for	0602 Ecology
utslib.for	0805 Distributed Computing
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Health
pubs.organisational-group	/University of Technology Sydney/Strength - CHSP - Health Services and Practice
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Centre for Health Technologies (CHT)
utslib.copyright.status	open_access	*
pubs.consider-herdc	false
dc.date.updated	2022-10-04T04:12:16Z
pubs.issue	13
pubs.publication-status	Published
pubs.volume	22
utslib.citation.issue	13

Abstract:

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.

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http://hdl.handle.net/10453/162290