Abstract
This study presents a deep learning-based approach
to predicting physiological health risks in athletes engaged in
contact sports using wearable sensor data. Motivated by the
need to detect early warning signs of collapse or severe fatigue,
this study employs a Long Short-Term Memory (LSTM) neural
network to analyse multivariate time-series data. Key
physiological signals, including heart rate, body temperature,
and motion, were extracted from the PAMAP2 dataset to train
and validate the model. The LSTM demonstrated strong
predictive performance, achieving an accuracy of 98.3% in
identifying potentially dangerous physiological states. In
addition to its high classification accuracy, the model effectively
captured temporal dependencies in the data, underscoring its
suitability for health risk prediction in dynamic, high-intensity
sports environments. This study highlights the potential of
wearable data and LSTM-based analysis in supporting
proactive athlete health management and injury prevention