ALGORITMI Center, School of Engineering - University of Minho, Guimarães, Portugal.
LIACC Artificial Intelligence and Computer Science Laboratory, Faculty of Engineering, University of Porto, Porto, Portugal.
J Med Syst. 2020 Jan 2;44(2):45. doi: 10.1007/s10916-019-1520-1.
There has been an increasing attention to the study of stress. Particularly, college students often experience high levels of stress that are linked to several negative outcomes concerning academic functioning, physical, and mental health. In this paper, we introduce the EuStress Solution, that aims to create an Information System to monitor and assess, continuously and in real-time, the stress levels of the students in order to predict burnout. The Information System will use a measuring instrument based on wearable device and machine learning techniques to collect and process stress-related data from the students without their explicit interaction. In the present study, we focus on heart rate and heart rate variability indices, by comparing baseline and stress condition. We performed different statistical tests in order to develop a complex and intelligent model. Results showed the neural network had the better model fit.
人们越来越关注压力的研究。特别是，大学生经常经历高水平的压力，这些压力与学术表现、身体和心理健康的几个负面结果有关。在本文中，我们介绍了 EuStress 解决方案，旨在创建一个信息系统，以持续实时地监测和评估学生的压力水平，以预测倦怠。信息系统将使用基于可穿戴设备和机器学习技术的测量仪器，在学生没有明确交互的情况下收集和处理与压力相关的数据。在本研究中，我们专注于心率和心率变异性指数，通过比较基线和压力条件。我们进行了不同的统计测试，以开发一个复杂和智能的模型。结果表明，神经网络具有更好的模型拟合度。
