Kán, PeterGerstweiler, GeorgSebernegg, AnnaKaufmann, HannesHasegawa, ShoichiSakata, NobuchikaSundstedt, Veronica2024-11-292024-11-292024978-3-03868-245-51727-530Xhttps://doi.org/10.2312/egve.20241363https://diglib.eg.org/handle/10.2312/egve20241363Analysis 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.Attribution 4.0 International LicenseCCS Concepts: Computing methodologies → Motion processing; Machine learning approaches; Human-centered computing → HCIComputing methodologies → Motion processingMachine learning approachesHumancentered computing → HCI10.2312/egve.2024136310 pages