Abstract

The increasing deployment of wearable and neurophysiological sensing technologies in elite sports enables continuous monitoring of athletes’ cognitive, physiological, and biomechanical states; however, existing approaches often analyse unimodal data and fail to capture complex cross-modal interactions that govern performance dynamics. This research proposes a multimodal neurophysiological and kinematic data fusion framework for predictive modelling of elite athlete performance dynamics. Data collection was conducted using wearable EEG headsets, heart rate monitors for heart rate variability (HRV), and inertial measurement units (IMUs) to capture kinematic parameters during training and competition across multiple elite sports. The collected high-frequency time-series data were transmitted through an IoT infrastructure to an edge–cloud platform for real-time monitoring and analytics. Pre-processing included band-pass filtering used to signal denoising removal for EEG and physiological signals. The proposed method employs a hybrid deep learning architecture that integrates Temporal Variational Autoencoder with Vanilla Recurrent Neural Network (TVAE-Vanilla RNN) model to predict elite athlete performance. The Intelligent Biosensor Dataset used in this study was collected using wearable EEG headsets, heart rate monitors for HRV, and inertial measurement units (IMUs) to capture kinematic parameters during training and competition across multiple elite sports.

Keywords

Multimodal Data Fusion, EEG, IMU Kinematics, Heart Rate Variability (HRV), Hybrid Deep Learning, Athlete Performance Prediction,

References

  1. Balsalobre-Fernández, C., Tejero-González, C.M., Del Campo-Vecino, J., Alonso-Curiel, D. (2015). Monitoring fatigue status with HRV measures in elite athletes. Frontiers in Physiology, 6, 343.
  2. Bock, M., Kuehne, H., Van Laerhoven, K., Moeller, M. (2024). Wear: An outdoor sports dataset for wearable and egocentric activity recognition. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 8(4), 1-21.
  3. Chen, J., Liu, W. (2026). Integration of IoT and Sensor Technology in Sports Performance Tracking and Analysis. Sensors and Materials, 38(1), 439-454.
  4. Chen, Q., Wang, J. (2025). Fatigue State Prediction of Athletes Based on Multi-Source Sensors. IEEE Access, 13, 176670–176681.
  5. Chen, Y., Li, S., Kuang, J., Zhang, X., Zhou, Z., Li, E.J., Chen, X. and Meng, X., 2025. Biomechanical Monitoring of Exercise Fatigue Using Wearable Devices: A Review. Bioengineering, 13(1), p.13.
  6. Gashi, S., Min, C., Montanari, A., Santini, S., Kawsar, F. (2022). A multidevice and multimodal dataset for human energy expenditure estimation using wearable devices. Scientific Data, 9(1), 537.
  7. Hoang, M.L. (2025). A Comprehensive Review of Machine Learning, and Deep Learning in Wearable iot Devices. IEEE Access, 13, 106035 – 106054.
  8. Jeong, J., Cho, J., Shim, K., Kwon, B., Lee, B., Lee, D., Lee, D., Lee, S. (2020). Multimodal signal dataset for 11 intuitive movement tasks from single upper extremity during multiple recording sessions. GigaScience, 9(10), giaa098.
  9. Ji, C., Zhong, Y., Gao, M. (2025). Multimodal fusion approach for sports injury prevention and pose keypoint detection. PLoS One, 20(8), e0327911.
  10. Jiao, Y. (2026). Validity and Challenges of Optical Heart Rate Sensors in Real-Time Classification of Exercise Intensity Zones. Sensor Review, 1-14.
  11. Kaggle, (2025) Multimodal gym sensor dataset for exercise monitoring. Kaggle.
  12. Kakhi, K., Jagatheesaperumal, S.K., Khosravi, A., Alizadehsani, R., Acharya, U.R. (2025). Fatigue monitoring using wearables and AI: Trends, challenges, and future opportunities. Computers in Biology and Medicine, 195, 110461.
  13. Kanatschnig, T., Schrapf, N., Leitner, L., et al. (2025). EEG theta and alpha oscillations during tactical decision-making: An examination of the neural efficiency hypothesis in volleyball. PLOS ONE, 20(2), e0318234.
  14. Li, S., Hou, Z., Amjad, K., Mushtaq, H. (2025). Multi modal fusion of medical imaging and biomechanical data using attention based swin-unet and LSTM for sports injury prediction. Frontiers in Physiology, 16, 1687895.
  15. Lobo, P., Morais, P., Murray, P., Vilaça, J.L. (2024). Trends and Innovations in Wearable Technology for Motor Rehabilitation, Prediction, and Monitoring: A Comprehensive Review. Sensors, 24(24), 7973.
  16. Madrigal-Cerezo, R., Domínguez-Sanz, N., Martín-Rodríguez, A. (2026). Wearable Biosensing and Machine Learning for Data-Driven Training and Coaching Support. Biosensors, 16(2), 97.
  17. Rahmani, M.H., Symons, M., Sobhani, O., Berkvens, R., Weyn, M. (2024). EMOWeAr: Wearable Physiological and Motion Dataset for Emotion Recognition and Context Awareness. Scientific Data, 11(1), 648.
  18. Schmitt, L., Regnard, J., Millet, G.P. (2015). Monitoring Fatigue Status with HRV Measures inElite Athletes: An Avenue beyond RMSSD? Frontiers in Physiology, 6, 343.
  19. Seong, M., Kim, G., Yeo, D., Kang, Y., Yang, H., DelPreto, J., Matusik, W., Rus, D., Kim, S. (2024). MultiSenseBadminton: Wearable Sensor–Based biomechanical dataset for evaluation of badminton performance. Scientific Data, 11(1), 343.
  20. Shah, S.T.A., Fernandes, J.M., Santos, J.P., Constantinescu, G., Pereira, A.B. (2026). The Micro-Mobility Sensing Gap: A Systematic Review of Physiological Safety Monitoring from Cycling to E-Scooters. Sensors, 26(4), 1110.
  21. Tan, D. (2025). CNN and LSTM-Based Multimodal Data Fusion for Performance Optimization in Aerobics Using Wearable Sensors. Informatica, 49(16).
  22. Xie, D., Wang, L. (2026). Design of a Sports Training System for Confined Spaces Based on Virtual Reality Technology. Journal of Cases on Information Technology (JCIT), 28(1), 1-16.
  23. Xue, X., Tang, Y. (2025). Multimodal AI-Driven Edge Framework for Energy-Efficient Human Activity Monitoring in Smart Grids. International Journal of Energy and Environmental Engineering, 16(02), 7.
  24. Yang, Y., Mohammadzadeh, F., Khishe, M., Ahmed, A. N., Abualhaj, M.M., Ghazal, T. M. (2025). Deep learning for sports motion recognition with a high-precision framework for performance enhancement. Scientific Reports, 15(1), 38861.
  25. Zhao, Y., Wu, J., Shen, P., Li, J., Xin, W., Bai, Y. (2025). A review of Wearable Flexible Sensors for Sports: from Materials to Applications. International Journal of Smart and Nano Materials, 16(4), 695-725.
  26. Ziya. (2025) A multimodal athlete performance sensor dataset for intelligent biosensor research. Kaggle.