The paper introduces a landmark-based approach for classifying injury-associated athlete sessions using minute-resolution multimodal football monitoring data. Instead of labeling every minute, it creates a single representation per athlete-session at fixed time points (landmarks) that incorporates information up to that moment, thereby aligning the session-level injury label with the data. Experiments on 3,743 elite women's football athlete-sessions from the 2020 SoccerMon dataset evaluate various representation strategies and machine learning models, reporting ROC-AUC values between 0.367 and 0.607 and PR-AUC values between 0.0080 and 0.0150, with notable uncertainty across landmarks.
By Evangelos Chatzidimitriou, Konstantinos Tserpes
The paper introduces a landmark‑based approach to classify injury‑associated athlete sessions using minute‑resolution multimodal football monitoring data. Instead of labeling every minute, it creates a single representation per athlete‑session at fixed time points (landmarks) based on information up to that moment, thereby aligning the session‑level injury label with the data. Experiments on 3,743 elite women’s football sessions show that cumulative and dynamic logistic regression models achieve ROC‑AUCs between 0.367 and 0.607, though results carry wide uncertainty.
The paper investigates 12‑class body‑only emotion recognition from skeleton motion using a leave‑performer‑out evaluation, where chance accuracy is 8.3% and a reproduced STGCN++ baseline scores 25.73% Macro‑F1. By ensembling eleven models with orthogonal error modes, the authors achieve 36.80% Macro‑F1, a 43% relative improvement over the baseline. They also introduce a tested explanation suite that demonstrates the ensemble’s decisions rely on motion‑grounded body‑region evidence, aligning strongly with Laban Movement Analysis attributes rather than classical kinematics, while showing diffuse temporal saliency.
By Naoto Nishida, Yoshio Ishiguro
MyoMechanix is a multimodal dataset and framework for action quality assessment that incorporates muscle activity and other physiological signals alongside visual data. It contains over 7,500 samples of 20 weight‑loaded actions from 38 subjects, with synchronized RGB video, 3D pose, sEMG, and additional signals. The accompanying Fitness Knowledge Graph structures expert annotations into relationships among actions, phases, key steps, errors, and corrective feedback, enabling compositional scoring and interpretable assessment through the CUBIST engine. The project also introduces MyoMechanix‑AQA, MyoMechanix‑VideoQA, and a novel MyoMechanix‑Video2EMG task, demonstrating that multimodal sensing and structured representations improve performance, interpretability, and error attribution.
By Hao Yin, Paritosh Parmar, Lijun Gu, Lin Xu, Tianxiao Guo, Xiujin Liu, Tianyou Zheng, Yang Zhang, Weiwei Fu
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:2605.14040v2 Announce Type: replace
Abstract: Trackable improvement in multimodal physics reasoning rests on a training-and-evaluation system that is itself rarely verified: the corpora a model...
By Shan Yang