Enhanced LSTM-based Approach for Predicting Heart Rate During Exercise

Aerobic endurance training is essential for athletes, but excessive training can cause severe injuries or death. Predicting heart rate load and adjusting training intensity is crucial. This paper proposes a deep neural network algorithm combining deep convolutional networks and long short-term memory (LSTM) networks for effective heart rate prediction. Experimental results show that the proposed model outperforms common deep learning models.