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Abstract：
The educational environment plays a vital role in the development of students who participate in athletic pursuits both in terms of their physical health and their ability to detect fatigue. As a result of recent advancements in deep learning and biosensors benefitting from edge computing resources, we are now able to monitor the physiological fatigue of students participating in sports in real time. These devices can then be used to analyze the data using contemporary technology. In this paper, we present an innovative deep learning framework for forecasting fatigue in athletic students following physical exercise. It addresses the issue of lack of precision computational models and extensive data analysis in current approaches to monitoring students\' physical activity. In our study, we classified fatigue and non-fatigue based on photoplethysmography (PPG) signals. Several deep learning models are compared in the study. Using limited training data, determining the optimal parameters for PPG presents a significant challenge. For datasets containing many data points, several models were trained using PPG signals: a deep residual network convolutional neural network (ResNetCNN) ResNetCNN, an Xception architecture, a bidirectional long short-term memory (BILSTM), and a combination of these models. Training and testing datasets were assigned using a fivefold cross validation approach. Based on the testing dataset, the model demonstrated a proper classification accuracy of 91.8%.
摘要：
教育环境在参与体育运动的学生的发展中起着至关重要的作用，无论是在身体健康还是检测疲劳的能力方面。由于深度学习和生物传感器的最新进展受益于边缘计算资源，我们现在能够实时监测参加运动的学生的生理疲劳。然后，这些设备可用于使用当代技术分析数据。在本文中,我们提出了一个创新的深度学习框架，用于预测运动学生在体育锻炼后的疲劳。它解决了当前监测学生身体活动的方法中缺乏精确的计算模型和广泛的数据分析的问题。在我们的研究中,我们根据光电容积描记(PPG)信号对疲劳和非疲劳进行了分类.在研究中比较了几种深度学习模型。使用有限的训练数据，确定PPG的最佳参数提出了重大挑战。对于包含许多数据点的数据集，使用PPG信号训练了几个模型：深度残差网络卷积神经网络(ResNetCNN)ResNetCNN，Xception架构，双向长短期记忆(BILSTM)，以及这些模型的组合。使用5倍交叉验证方法分配训练和测试数据集。根据测试数据集，该模型显示出91.8%的正确分类准确率。
