• Shivakumar Swamy N
• Preethi B S
• Manjunath R

Fatigue is a common issue in sports that can lead to injuries and decrease athlete performance. This paper proposes a deep learning-based system for fatigue prediction in sports using GPS and RPE data. The proposed system can accurately predict fatigue levels in athletes and can be useful for coaches and sports scientists in optimizing athlete performance and preventing injuries. The proposed system uses a combination of GPS and RPE data for fatigue prediction, which provides a more comprehensive picture of the athlete's physical condition and fatigue level. The deep learning approach used in the proposed model can handle complex and nonlinear relationships between the input features and the output variable. The model architecture consists of several convolutional and recurrent layers, which can learn and extract meaningful features from the input data. Experimental results show that the proposed system achieved high accuracy and precision in predicting fatigue levels in athletes. The system's performance was evaluated using various evaluation metrics such as accuracy, precision, mean absolute error (MAE), and root mean square error (RMSE). In conclusion, the proposed system is a promising approach for fatigue prediction in sports using GPS and RPE data. The system overcomes several limitations of traditional machine learning approaches and provides a more comprehensive picture of the athlete's physical condition and fatigue level. The proposed system can be useful for coaches and sports scientists in optimizing athlete performance and preventing injuries, thus contributing to the development of sports science and technology.

Fatigue prediction, Sports performance, GPS data, RPE data, Machine learning, Deep learning, Convolutional neural networks, Recurrent neural networks, Feature extraction, Data analysis, Training load, Athlete monitoring, Injury prevention

"A Hybrid Deep Learning Model for Real-time Fatigue Prediction in Sports Utilizing GPS Data and Rate of Perceived Exertion", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 6, page no.k632-k638, June-2023, Available :http://www.jetir.org/papers/JETIR2306A82.pdf

"A Hybrid Deep Learning Model for Real-time Fatigue Prediction in Sports Utilizing GPS Data and Rate of Perceived Exertion", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 6, page no. ppk632-k638, June-2023, Available at : http://www.jetir.org/papers/JETIR2306A82.pdf