From Sprint to Recovery: LSTM-Powered Heart Rate Recovery Forecasting in HIIT Sessions

In recent years, the growing interest in applying artificial intelligence to the healthcare domain, especially in the monitoring and control of health status during fitness activities, has opened new opportunities in understanding and enhancing human performance and health. This interdisciplinary approach, merging cutting-edge AI with exercise physiology, offers promising avenues for personalized healthcare, optimized athletic training, and advanced health monitoring techniques. Current study addressing a critical aspect of exercise physiology: the forecasting of heart rate (HR) recovery patterns following high-intensity intervals. In pursuit of this objective, a comprehensive deep learning framework is developed, designed to forecast HR recovery patterns. This system integrates signal processing techniques combined with advanced deep learning architectures to facilitate real-time HR measurements and predict future HR dynamics during high-intensity interval training. Central to the proposed approach is a long short-term memory (LSTM) based encoder-decoder architecture. To enhance the model's accuracy and robustness, a task-specific loss function is employed. This function not only calculates standard HR errors but also incorporates HR pattern slopes and angles. This approach has achieved promising results, with the model demonstrating strong performance. The mean absolute error in HR forecasting is 3.5 bpm for the encoder and 3.8 bpm for the decoder parts.