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Using Intermediate Models and Knowledge Learning to Improve Stress Prediction

  • Alban Maxhuni
  • Pablo Hernandez-Leal
  • Eduardo F. MoralesEmail author
  • L. Enrique Sucar
  • Venet Osmani
  • Angelica Muńoz-Meléndez
  • Oscar Mayora
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 179)

Abstract

Motor activity in physical and psychological stress exposure has been studied almost exclusively with self-assessment questionnaires and from reports that derive from human observer, such as verbal rating and simple descriptive scales. However, these methods are limited in objectively quantifying typical behaviour of stress. We propose to use accelerometer data from smartphones to objectively quantify stress levels. Used data was collected in real-world setting, from 29 employees in two different organisations over 5 weeks. To improve classification performance we propose to use intermediate models. These intermediate models represent the mood state of a person which is used to build the final stress prediction model. In particular, we obtained an accuracy of 78.2 % to classify stress levels.

Keywords

Motor activity Stress prediction Smartphones 

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Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2017

Authors and Affiliations

  • Alban Maxhuni
    • 1
  • Pablo Hernandez-Leal
    • 2
  • Eduardo F. Morales
    • 2
    Email author
  • L. Enrique Sucar
    • 2
  • Venet Osmani
    • 3
  • Angelica Muńoz-Meléndez
    • 2
  • Oscar Mayora
    • 3
  1. 1.DISIUniversity of TrentoTrentoItaly
  2. 2.INAOE-Instituto Nacional de Astrofísica, Óptica y ElectrónicaPueblaMexico
  3. 3.CREATE-NETTrentoItaly

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