Towards real-time automatic stress detection for office workplaces

Franci Suni Lopez, Nelly Condori-Fernandez*, Alejandro Catala

*Corresponding author for this work

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

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In recent years, several stress detection methods have been proposed, usually based on machine learning techniques relying on obstructive sensors, which could be uncomfortable or not suitable in many daily situations. Although studies on emotions are emerging and rising in Software Engineering (SE) research, stress has not been yet well investigated in the SE literature despite its negative impact on user satisfaction and stakeholder performance. In this paper, we investigate whether we can reliably implement a stress detector in a single pipeline suitable for real-time processing following an arousal-based statistical approach. It works with physiological data gathered by the E4-wristband, which registers electrodermal activity (EDA). We have conducted an experiment to analyze the output of our stress detector with regard to the self-reported stress in similar conditions to a quiet office workplace environment when users are exposed to different emotional triggers.

Original languageEnglish
Title of host publicationInformation Management and Big Data
Subtitle of host publication5th International Conference, SIMBig 2018, Lima, Peru, September 3 – 5, 2018, Proceedings
EditorsJuan Antonio Lossio-Ventura, Hugo Alatrista-Salas, Denisse Muñante
PublisherSpringer Verlag
Number of pages16
ISBN (Electronic)9783030116804
ISBN (Print)9783030116798
Publication statusPublished - 2019
Event5th International Conference on Information Management and Big Data, SIMBig 2018 - Lima, Peru
Duration: 3 Sept 20185 Sept 2018

Publication series

NameCommunications in Computer and Information Science
ISSN (Print)1865-0929


Conference5th International Conference on Information Management and Big Data, SIMBig 2018


  • Emotional trigger
  • Physiological data
  • Stress detection


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