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.
arXiv:2609.26923v1 Announce Type: cross Abstract: Cricket is one of the most celebrated sports world-wide, and technological advancement has become deeply embedded in how the modern game is analyzed...
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.
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.
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.
The study introduces a framework that reconciles minute‑resolution athlete monitoring data with injury labels that are only available at the session level. By creating fixed‑time landmarks (10, 20, 30, 40, 50, and 60 minutes) and generating a single representation per athlete‑session up to each landmark, the authors evaluate several machine‑learning models and data‑augmentation strategies on elite women’s football data. Results show that discrimination varies across landmarks, with TabPFN outperforming Logistic Regression at later landmarks but not consistently beating Random Forest, and that synthetic augmentation offers benefits only in specific conditions.
arXiv:2609.07617v1 Announce Type: new Abstract: With the rise of live sports betting in recent years, tennis forecasting has expanded from pre-match prediction to models that update win probabilities...
arXiv:2608. 08736v1 Announce Type: new Abstract: Fitness Action Quality Assessment (AQA) is important for intelligent sports training, yet the capabilities of Multimodal Large Language Models (MLLMs) in this setting remain underexplored.
arXiv:2202. 14019v3 Announce Type: replace-cross Abstract: Maintaining proper form while exercising is important for preventing injuries and maximizing muscle mass gains.
arXiv:2606. 28570v1 Announce Type: cross Abstract: Athlete assessment is a critical process for tracking physical progress and identifying elite talent.
arXiv:2607. 16597v1 Announce Type: new Abstract: The rapid digitalisation of elite sport has created new opportunities for integrating artificial intelligence (AI), performance analytics, and decision-support systems into athlete development and competition management.
Skill Profiling with Attributable Reasoning (SPAR) is a wearable system that uses an eight‑IMU garment and pressure‑insole sensors to classify boxing punches as expert or novice. It provides multi‑tier explanations—joint‑level attributions for analysts, counterfactual kinetic‑chain layers for coaches, and plain‑language narratives for athletes—to enable actionable feedback. In a study of 17 participants and 4,713 punches, SPAR achieved a leave‑one‑participant‑out AUC of 0.842, and thematic analysis of coach interviews identified six key feedback themes.
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.