Detection of Different Throw Types and Ball Velocity With IMUs and Machine Learning in Team Handball

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aut.relation.conferenceInternational Society of Biomechanics in Sport annual conferenceen_NZ
aut.researcherStewart, Thomas
dc.contributor.authorvan den Tillaar, Ren_NZ
dc.contributor.authorBhandurge, Sen_NZ
dc.contributor.authorStewart, Ten_NZ
dc.date.accessioned2020-09-04T01:46:34Z
dc.date.available2020-09-04T01:46:34Z
dc.date.copyright2020-07-18en_NZ
dc.date.issued2020-07-18en_NZ
dc.description.abstractThe purpose of this study was to investigate if an inertial measurement unit (IMU) and machine learning could be used to detect different types of team handball throws and predict ball velocity. Throwing was measured using IMUs and a radar gun in seventeen participants during standing, running and jump throws with a circular and whip-like wind up. Using these data, machine learning could predict peak ball velocity with an error of 1.05 m/s and classify approach types and throw types with ~85–90% accuracy. It was concluded that to monitor throwing load, the combination of inertial measurement units and machine learning offers a practical and automated method of quantifying throw counts and discriminating throw types in handball players under standard conditions.
dc.identifier.citationISBS Proceedings Archive: Vol. 38 : Iss. 1 , Article 48. Available at: https://commons.nmu.edu/isbs/vol38/iss1/48
dc.identifier.urihttps://hdl.handle.net/10292/13633
dc.publisherInternational Society of Biomechanics in Sport (ISBS)
dc.relation.urihttps://commons.nmu.edu/isbs/vol38/iss1/48/
dc.rightsThe following uses are always permitted to the author(s) and do not require further permission from provided the author does not alter the format or content of the articles, including the copyright notification: Posting of the article on the internet as part of a non-commercial open access institutional repository or other non-commercial open access publication site affiliated with the author(s)'s place of employment.
dc.rights.accessrightsOpenAccessen_NZ
dc.subjectThrowing velocity; Artificial intelligence
dc.titleDetection of Different Throw Types and Ball Velocity With IMUs and Machine Learning in Team Handballen_NZ
dc.typeConference Contribution
pubs.elements-id390436
pubs.organisational-data/AUT
pubs.organisational-data/AUT/Health & Environmental Science
pubs.organisational-data/AUT/Health & Environmental Science/Sports & Recreation
pubs.organisational-data/AUT/Health & Environmental Science/SPRINZ
pubs.organisational-data/AUT/PBRF
pubs.organisational-data/AUT/PBRF/PBRF Health and Environmental Sciences
pubs.organisational-data/AUT/PBRF/PBRF Health and Environmental Sciences/HS Sports & Recreation 2018 PBRF
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