Towards real-time automatic stress detection for office workplaces

Franci Suni Lopez, Nelly Condori-Fernandez, Alejandro Catala

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

3 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

Nota bibliográfica

Publisher Copyright:
© 2019, Springer Nature Switzerland AG.


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