Information System for Monitoring and Assessing Stress Among Medical Students

  • Eliana Silva
  • Joyce AguiarEmail author
  • Luís Paulo Reis
  • Jorge Oliveira e Sá
  • Joaquim Gonçalves
  • Victor Carvalho
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 931)


The severe or prolonged exposure to stress-inducing factors in occupational and academic settings is a growing concern. The literature describes several potentially stressful moments experienced by medical students throughout the course, affecting cognitive functioning and learning. 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 individuals in order to predict chronic stress. The Information System will use a measuring instrument based on wearable devices and machine learning techniques to collect and process stress-related data from the individual without his/her explicit interaction. A big database has been built through physiological, psychological, and behavioral assessments of medical students. In this paper, we focus on heart rate and heart rate variability indices, by comparing baseline and stress condition. In order to develop a predictive model of stress, we performed different statistical tests. Preliminary results showed the neural network had the better model fit. As future work, we will integrate salivary samples and self-report questionnaires in order to develop a more complex and intelligent model.


Stress Heart rate variability Wearable devices Big data mining Medical students 



This work was funded by projects: the EUSTRESS (Sistema de Informação para a Monitorização e Avaliação dos Níveis do Stress e Previsão de Stress Crónico”), funded by European Regional Development Fund and by National Funds through the Portuguese Foundation for Science and Technology, within NUP = NORTE-01-0247-FEDER-017832; and the QVida+ project (Estimação Contínua de Qualidade de Vida para Auxílio Eficaz à Decisão Clínica), funded by European Structural funds (FEDER-003446), supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Eliana Silva
    • 1
  • Joyce Aguiar
    • 1
    Email author
  • Luís Paulo Reis
    • 2
  • Jorge Oliveira e Sá
    • 1
  • Joaquim Gonçalves
    • 3
    • 4
  • Victor Carvalho
    • 4
  1. 1.ALGORITMI Research CentreUniversity of MinhoGuimarãesPortugal
  2. 2.Artificial Intelligence and Computer Science Laboratory (LIACC), Faculty of EngineeringUniversity of PortoPortoPortugal
  3. 3.2AI/EST/IPCA – Technology School of Polytechnic Institute of Cávado and AveBarcelosPortugal
  4. 4.LITEC – Innovation and Knowledge Engineering LabPortoPortugal

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