The paper introduces PART, a multimodal predictive framework for tennis that combines physiological, training, sleep, questionnaire, jump, and video data from nine collegiate players to assess overall wellness, injury risk, physical capability, and playing style. Using machine learning and deep learning, PART provides holistic athlete assessments and forecasts specific injury risks to body areas such as elbows and knees. Evaluation shows strong performance in predicting wellness and injury risk, with potential benefits for recreational players who often injure themselves due to poor technique.
By Francisco Erramuspe Alvarez, Shobharani Polasa, Weihao Qu, Jay Wang, Ling Zheng
arXiv:2608. 19646v1 Announce Type: new Abstract: Visual understanding in sports has emerged as a hot topic in computer vision in recent years.
By Yunhao Zhao, Haoying Sun, Jiarui Li, Zhuming Wang, Ya Jing, Xiangbo Shu, Lifang Wu, Changwen Chen
arXiv:2609.26056v1 Announce Type: new
Abstract: Vision-Language Models (VLMs) offer promising capabilities for automated sports coaching but face a fundamental limitation: they implicitly compare aga...
By Agamdeep Singh, Sujit PB, Mayank Vatsa
arXiv:2605.01234v2 Announce Type: replace
Abstract: We present TT4D, a large-scale, high-fidelity table tennis dataset. It provides $140+$ hours of reconstructed singles and doubles gameplay from mon...
By Nima Rahmanian, Daniel Kienzle, Thomas Gossard, Dvij Kalaria, Rainer Lienhart, Shankar Sastry
arXiv:2609.39300v1 Announce Type: new
Abstract: Recent advances in computer vision have made broadcast sports videos increasingly useful for event analysis, performance assessment, and player-safety...
By Ahmed Endris Hasen, Muhammad Shahzad Khan, Nikolaos Passalis, Jenni Raitoharju
arXiv:2606. 09842v1 Announce Type: cross Abstract: Applying Human Pose Estimation (HPE) in real world environments remains a challenging task, this paper explores and surveys real time HPE approaches and their limitations in sports analysis for individuals, alongside developing a practical lightweight prototype for real world testing and usage.
By Parth Agrawal, Ronit, Sagar Kumar, Aashish Bhambri
arXiv:2609.28049v1 Announce Type: cross
Abstract: Video understanding is usually benchmarked on curated, single-actor, or professionally filmed clips, and a strong score there is routinely read as ev...
By Sai Varun Kodathala, Prashanth Pollishetty, Jaylen Cargill
arXiv:2606. 28570v1 Announce Type: cross Abstract: Athlete assessment is a critical process for tracking physical progress and identifying elite talent.
By Deep Ghosal, Ishani Sen, Wazib Ansar, Amlan Chakrabarti
arXiv:2606. 31127v1 Announce Type: cross Abstract: To enable personalized, real-time coaching using Augmented Reality glasses or fixed camera setups in domains such as sports, cooking, or music, a system must understand not just what a person does, but how well they execute an activity.
By Bj\"orn Braun, Christian Holz
Video understanding is usually benchmarked on curated, single-actor, or professionally filmed clips, and a strong score there is routinely read as evidence a model is robust enough for deployment. Ama...
arXiv:2608. 07932v2 Announce Type: replace Abstract: Sports video analysis is crucial for athletic analytics and broadcasting enhancement.
By Yizhi Li, Jiawei Jiang, Guanhong Wang, Yingcai Wu, Gaoang Wang
arXiv:2202. 14019v3 Announce Type: replace-cross Abstract: Maintaining proper form while exercising is important for preventing injuries and maximizing muscle mass gains.
By Paritosh Parmar, Amol Gharat, Helge Rhodin