Deep Learning-Driven Assessment of Student Movement and Performance Using Physiological Data in Physical Education Information Systems: An S-AIoT Solution

Abstract

This study bridges a crucial gap in athletic performance analysis by introducing a novel machine learning (ML) framework that leverages integrated physiological signals (from the DB 2.0 database) towards Sport Artificial Intelligence of Things (S-AIoT). Understanding athletic performance is key to developing effective training programs and enhancing overall physical education. However, traditional methods often fall short in capturing the nuances of human movement. Our primary goal is to develop an innovative method for accurately classifying sports activities using advanced analytical techniques that consider various physiological signals. This study aims to improve classification accuracy and provide real-time analytics for sports performance. To achieve this, we employ spatial and temporal attention mechanisms to dynamically weight critical signals, enabling precise tracking of movement transitions across different sports. The model is trained on comprehensive datasets comprising respiration rate, ECG, and heart rate (HR), providing a multifaceted analysis of athletic performance. Extensive experiments validate the model, which achieves a remarkable accuracy of 90.32%. It is the first model of its kind, outperforming established models like 1D convolutional neural network (CNN), LSTM, BiLSTM, and 1D CNN-BiLSTM. The model demonstrates strong generalization ability on unseen data, proving its effectiveness in diverse scenarios, and exhibits moderate noise resilience, enhancing its practical applicability.