[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119479-en":3,"doc-seo-119479-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119479,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Analysis of Tennis Forehand Technique using Machine Learning","Analysis of human motion supports sports coaching, artistic practice, and rehabilitation, but automatic assessment often struggles with identifying incorrect technique. This paper introduces a data-driven machine learning method for evaluating tennis forehand technique using trainer-defined training rules. Motions are aligned to reference performances via dynamic time warping, enabling motion phase detection and feature extraction. Two supervised learning variants are tested—uninformed learning vs informed learning that incorporates tennis methodology features and phases—showing higher accuracy and faster runtime for informed learning, validated through quantitative evaluation and a qualitative expert study.","International Conference on Artificial Reality and Telexistence Eurographics Symposium on Virtual Environments (2024)  \nS. Hasegawa, N. Sakata and V. Sundstedt (Editors)  \nAnalysis of Tennis Forehand Technique using Machine Learning  \nP. Kán 1, G. Gerstweiler2 , A. Sebernegg2 , and H. Kaufmann 1  \n1Institute of Visual Computing and Human-Centered Technology, TU Wien, Austria  \n2VR Motion Learning GmbH & Co KG, Austria  \nFigure 1: Visual feedback of the trainee’s motion analysis by our method. The left panel shows correctness of performing individual training  \nrules and the right window plays a 3D replay of the captured trainee’s motion (blue) in comparison to a professional motion (green) . These two motions are temporally aligned using dynamic time warping. When a user selects a specific training rule from the left window, a textual explanation of this rule is shown to help the user improve this specific aspect of the technique.  \nAbstract  \nAnalysis 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  \nsupervised 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.  \nCCS Concepts  \n• Computing methodologies → Motion processing; Machine learning approaches; • Human-centered computing → HCI;  \n1. Introduction  \nKnowledge of correct sport technique plays an important role for injury prevention and performance improvement in sports both for beginners and professionals. While the best way to practice sport technique is with a professional trainer on-site, this option is often inaccessible due to the time and cost constraints. Therefore,  \nmethods for automatic assessment of sport technique and guidance have been emerging in recent years [MAKD17, HGH∗18, SNT18, AKF∗24] . While these methods enable computer-aided motion analysis, they often focus on motion type classification and do not analyze if the motion is performed biomechanically correct with respect to the technique of a given sport and with relation to its training methodology.  \n© 2024 The Authors.  \nProceedings published by Eurographics-The European Association for Computer Graphics.  \nThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.  \nDOI: 10.2312/egve.20241363  \n2 of 10 P. Kán, G. Gerstweiler, A. Sebernegg, & H. Kaufmann / Analysis of Tennis Forehand Technique using Machine Learning  \nWe address the task of motion analysis, for the case of tennis forehand technique, by a data-driven approach utilizing machine learning and apriori information from trainers. This apriori information consists of the definition of training rules and motion phases for a tennis forehand stroke. Furthermore, specific motion features such as distance ratios are created in addition to positions, rotations, and velocities to augment the motion data with richer information for technique correctness classifica","cbCaiebOPYqhr4A8","https://ap.wps.com/l/cbCaiebOPYqhr4A8","pdf",664470,1,10,"English","en",105,"# Introduction\n## Automatic assessment and technique correctness\n## Training rules and motion phases\n## Motion alignment with dynamic time warping\n## Experimental evaluation and comparison","[{\"question\":\"What is the main goal of this paper on tennis forehand analysis?\",\"answer\":\"The work aims to automatically assess tennis forehand technique correctness by addressing incorrect technique identification using a machine learning approach informed by trainer knowledge.\"},{\"question\":\"How does the method handle comparing a trainee motion with a reference motion?\",\"answer\":\"It aligns the motions in time using dynamic time warping, then identifies motion phases and extracts motion features for technique correctness classification.\"},{\"question\":\"What is the difference between informed and uninformed learning in the experiments?\",\"answer\":\"Both are supervised learning, but informed learning additionally uses motion features and motion phases derived from tennis training methodology, while uninformed learning uses available motion features over the whole duration.\"}]","Analysis of Tennis Forehand Technique using Machine Learning | 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is the main goal of this paper on tennis forehand analysis?","Question",{"text":75,"@type":76},"The work aims to automatically assess tennis forehand technique correctness by addressing incorrect technique identification using a machine learning approach informed by trainer knowledge.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the method handle comparing a trainee motion with a reference motion?",{"text":80,"@type":76},"It aligns the motions in time using dynamic time warping, then identifies motion phases and extracts motion features for technique correctness classification.",{"name":82,"@type":73,"acceptedAnswer":83},"What is the difference between informed and uninformed learning in the experiments?",{"text":84,"@type":76},"Both are supervised learning, but informed learning additionally uses motion features and motion phases derived from tennis training methodology, while uninformed learning uses available motion features over the whole 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