Title: Athlete fatigue recognition and performance analysis via multimodal deep learning with cross-modal attention mechanisms

Authors: Xue Han; Fujiang Cui; Feng Wang; Wei Wang; Haibo Wang

Addresses: Department of Health Management and Services, Cangzhou Medical College, Cangzhou, 061001, He Bei, China ' Department of Health Management and Services, Cangzhou Medical College, Cangzhou, 061001, He Bei, China ' Orthopedic Joint Surgery, Cangzhou Hospital of Integrated Traditional Chinese and Western Medicine, Cangzhou Medical College, Cangzhou, 061001, He Bei, China ' Department of Health Management and Services, Cangzhou Medical College, Cangzhou, 061001, He Bei, China ' Department of Physical Education, Wuxi Institute of Technology, Wuxi 214121, Jiangsu, China

Abstract: In modern sport science, timely detection of athlete fatigue and accurate assessment of performance degradation are critical for enhancing training efficiency, reducing injury risk, and optimising competitive outcomes. This study proposes a multimodal deep learning framework with attention mechanisms that integrates physiological (e.g., ECG/EMG), biomechanical (e.g., IMU motion data) and visual (e.g., body pose or facial cues) modalities for simultaneous fatigue-state classification and continuous performance regression. The designed architecture employs dedicated encoders per modality, followed by a crossmodal attention fusion module and dualtask heads for classification and regression. Experiments conducted on publicly available datasets demonstrate that the proposed method outperforms eight baseline algorithms, achieving 95.76% accuracy in fatigue recognition and 0.108 MAE in performance prediction. Ablation studies confirm that each modality and the attention fusion contribute significantly to overall performance.

Keywords: athlete fatigue recognition; multimodal deep learning; attention mechanism; performance regression; cross-modal fusion.

DOI: 10.1504/IJBIDM.2026.155251

International Journal of Business Intelligence and Data Mining, 2026 Vol.28 No.4/5/6, pp.348 - 361

Received: 02 Aug 2025
Accepted: 01 Mar 2026
Published online: 29 Jul 2026 *

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