Tennis Stroke Classification: Comparing Wrist and Racket as IMU Sensor Position

dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.authorFindling, Rainharden_US
dc.contributor.authorEbner, Christopher J.en_US
dc.contributor.departmentAmbient Intelligenceen_US
dc.contributor.departmentUniversity of Applied Sciences Upper Austriaen_US
dc.contributor.departmentDepartment of Communications and Networkingen
dc.date.accessioned2021-03-22T07:12:08Z
dc.date.available2021-03-22T07:12:08Z
dc.date.issued2019-12en_US
dc.description.abstractAutomatic tennis stroke recognition can help tennis players improve their training experience. Previous work has used sensors positions on both wrist and tennis racket, of which different physiological aspects bring different sensing capabilities. However, no comparison of the performance of both positions has been done yet. In this paper we comparatively assess wrist and racket sensor positions for tennis stroke detection and classification. We investigate detection and classification rates with 8 well-known stroke types and visualize their differences in 3D acceleration and angular velocity. Our stroke detection utilizes a peak detection with thresholding and windowing on the derivative of sensed acceleration, while for our stroke recognition we evaluate different feature sets and classification models. Despite the different physiological aspects of wrist and racket as sensor position, for a controlled environment results indicate similar performance in both stroke detection (98.5%-99.5%) and user-dependentand independent classification (89%-99%).en
dc.description.versionPeer revieweden
dc.format.extent10
dc.format.extent74–83
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationFindling , R & Ebner , C J 2019 , Tennis Stroke Classification: Comparing Wrist and Racket as IMU Sensor Position . in 17th International Conference on Advances in Mobile Computing & Multimedia (MoMM2019) . ACM , pp. 74–83 , International Conference on Advances in Mobile Computing and Multimedia , Munich , Germany , 02/12/2019 . https://doi.org/10.1145/3365921.3365929en
dc.identifier.doi10.1145/3365921.3365929en_US
dc.identifier.isbn978-1-4503-7178-0
dc.identifier.otherPURE UUID: cc1d4da3-f508-45fb-b732-e59a9f44acb1en_US
dc.identifier.otherPURE ITEMURL: https://research.aalto.fi/en/publications/cc1d4da3-f508-45fb-b732-e59a9f44acb1en_US
dc.identifier.otherPURE FILEURL: https://research.aalto.fi/files/37133508/ELEC_Ebner_Tennis_Stroke_Recognition_MoMM2019.pdfen_US
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/103287
dc.identifier.urnURN:NBN:fi:aalto-202103222565
dc.language.isoenen
dc.relation.ispartofInternational Conference on Advances in Mobile Computing and Multimediaen
dc.relation.ispartofseries17th International Conference on Advances in Mobile Computing & Multimedia (MoMM2019)en
dc.rightsopenAccessen
dc.subject.keywordmachine learningen_US
dc.subject.keywordtennis stroke detectionen_US
dc.subject.keywordtennis stroke recognitionen_US
dc.subject.keywordwearable sensorsen_US
dc.titleTennis Stroke Classification: Comparing Wrist and Racket as IMU Sensor Positionen
dc.typeConference article in proceedingsfi
dc.type.versionacceptedVersion
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