Towards real-time automatic stress detection for office workplaces

Franci Suni Lopez, Nelly Condori-Fernandez, Alejandro Catala

    Resultado de la investigación: Capítulo del libro/informe/acta de congresoArticulo (Contribución a conferencia)revisión exhaustiva

    8 Citas (Scopus)


    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.

    Idioma originalInglés
    Título de la publicación alojadaInformation Management and Big Data - 5th International Conference, SIMBig 2018, Proceedings
    EditoresJuan Antonio Lossio-Ventura, Hugo Alatrista-Salas, Denisse Muñante
    EditorialSpringer Verlag
    Número de páginas16
    ISBN (versión impresa)9783030116798
    EstadoPublicada - 2019
    Evento5th International Conference on Information Management and Big Data, SIMBig 2018 - Lima, Perú
    Duración: 3 set. 20185 set. 2018

    Serie de la publicación

    NombreCommunications in Computer and Information Science
    ISSN (versión impresa)1865-0929


    Conferencia5th International Conference on Information Management and Big Data, SIMBig 2018


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