Analysis of Tennis Forehand Technique using Machine Learning

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dc.contributor.authorKán, Peteren_US
dc.contributor.authorGerstweiler, Georgen_US
dc.contributor.authorSebernegg, Annaen_US
dc.contributor.authorKaufmann, Hannesen_US
dc.contributor.editorHasegawa, Shoichien_US
dc.contributor.editorSakata, Nobuchikaen_US
dc.contributor.editorSundstedt, Veronicaen_US
dc.date.accessioned2024-11-29T06:42:57Z
dc.date.available2024-11-29T06:42:57Z
dc.date.issued2024
dc.description.abstractAnalysis of human motion is instrumental in many areas including sports, arts, and rehabilitation. This paper presents a novel method for human motion analysis with the focus on tennis training and forehand technique assessment. We address the problems of automatic motion analysis and incorrect technique identification by a machine learning approach. We utilize the concept of training rules that are used to individually assess specific aspects of a given type of motion. Our method for motion analysis is based on insights from professional trainers and our training rules are co-designed with them. The presented method is evaluated quantitatively using recorded dataset of tennis forehand motions. This evaluation compares two variants of sport technique correctness classification: informed and uninformed learning. Both learning variants fall into the category of supervised learning, but informed learning additionally utilizes motion features and motion phases derived from tennis training methodology. Our experiments suggest that informed learning leads to higher accuracy and faster speed of the algorithm. Finally, we studied our method in a qualitative expert study.en_US
dc.description.sectionheadersTraining and Augmentation
dc.description.seriesinformationICAT-EGVE 2024 - International Conference on Artificial Reality and Telexistence and Eurographics Symposium on Virtual Environments
dc.identifier.doi10.2312/egve.20241363
dc.identifier.isbn978-3-03868-245-5
dc.identifier.issn1727-530X
dc.identifier.pages10 pages
dc.identifier.urihttps://doi.org/10.2312/egve.20241363
dc.identifier.urihttps://diglib.eg.org/handle/10.2312/egve20241363
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Computing methodologies → Motion processing; Machine learning approaches; Human-centered computing → HCI
dc.subjectComputing methodologies → Motion processing
dc.subjectMachine learning approaches
dc.subjectHuman
dc.subjectcentered computing → HCI
dc.titleAnalysis of Tennis Forehand Technique using Machine Learningen_US
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