TY - GEN
T1 - Towards real-time automatic stress detection for office workplaces
AU - Suni Lopez, Franci
AU - Condori-Fernandez, Nelly
AU - Catala, Alejandro
N1 - Funding Information:
Acknowledgments. Authors would like to thank to Dirk Heylen, head of HMI Lab of University of Twente, for facilitating us the HMI Lab to conduct the experiments and his early feedback. Also, We thank all the participants who took part in our research. This work has been supported by grant 234-2015-FONDECYT (Master Program) from Cienciactiva of the National Council for Science, Technology and Technological Innovation (CONCYTEC-PERU). Moreover, this work has received financial support from the Spanish Ministry of Economy, Industry and Competitiveness with the Project: TIN2016-78011-C4-1-R; Council of Culture, Education and University Planning with the project ED431G/08, the European Regional Development Fund (ERDF).
Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Emotional trigger
KW - Physiological data
KW - Stress detection
UR - http://www.scopus.com/inward/record.url?scp=85063522016&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-11680-4_27
DO - 10.1007/978-3-030-11680-4_27
M3 - Articulo (Contribución a conferencia)
AN - SCOPUS:85063522016
SN - 9783030116798
T3 - Communications in Computer and Information Science
SP - 273
EP - 288
BT - Information Management and Big Data - 5th International Conference, SIMBig 2018, Proceedings
A2 - Lossio-Ventura, Juan Antonio
A2 - Alatrista-Salas, Hugo
A2 - Muñante, Denisse
PB - Springer Verlag
T2 - 5th International Conference on Information Management and Big Data, SIMBig 2018
Y2 - 3 September 2018 through 5 September 2018
ER -