Towards real-time automatic stress detection for office workplaces

Franci Suni Lopez; Nelly Condori-Fernandez; Alejandro Catala

doi:10.1007/978-3-030-11680-4_27

Towards real-time automatic stress detection for office workplaces

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

^*Corresponding author for this work

Software and Sustainability (S2)

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

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Abstract

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 language	English
Title of host publication	Information Management and Big Data
Subtitle of host publication	5th International Conference, SIMBig 2018, Lima, Peru, September 3 – 5, 2018, Proceedings
Editors	Juan Antonio Lossio-Ventura, Hugo Alatrista-Salas, Denisse Muñante
Publisher	Springer Verlag
Pages	273-288
Number of pages	16
ISBN (Electronic)	9783030116804
ISBN (Print)	9783030116798
DOIs	https://doi.org/10.1007/978-3-030-11680-4_27
Publication status	Published - 2019
Event	5th International Conference on Information Management and Big Data, SIMBig 2018 - Lima, Peru Duration: 3 Sept 2018 → 5 Sept 2018

Publication series

Name	Communications in Computer and Information Science
Volume	898
ISSN (Print)	1865-0929

Conference

Conference	5th International Conference on Information Management and Big Data, SIMBig 2018
Country/Territory	Peru
City	Lima
Period	3/09/18 → 5/09/18

Funding

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).

Funders	Funder number
CONCYTEC-PERU
Council of Culture, Education and University Planning	ED431G/08
Ministry of Economy, Trade and Industry	TIN2016-78011-C4-1-R
Ministry of Economy, Trade and Industry
European Regional Development Fund
Consejo Nacional de Ciencia, Tecnología e Innovación Tecnológica

Keywords

Emotional trigger
Physiological data
Stress detection

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10.1007/978-3-030-11680-4_27

Towards real-time automatic stress detection for office workplacesFinal published version, 1.49 MBLicence: Article 25fa Dutch Copyright Act

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Suni Lopez, F., Condori-Fernandez, N., & Catala, A. (2019). Towards real-time automatic stress detection for office workplaces. In J. A. Lossio-Ventura, H. Alatrista-Salas, & D. Muñante (Eds.), Information Management and Big Data: 5th International Conference, SIMBig 2018, Lima, Peru, September 3 – 5, 2018, Proceedings (pp. 273-288). (Communications in Computer and Information Science; Vol. 898). Springer Verlag. https://doi.org/10.1007/978-3-030-11680-4_27

Suni Lopez, Franci ; Condori-Fernandez, Nelly ; Catala, Alejandro. / Towards real-time automatic stress detection for office workplaces. Information Management and Big Data: 5th International Conference, SIMBig 2018, Lima, Peru, September 3 – 5, 2018, Proceedings. editor / Juan Antonio Lossio-Ventura ; Hugo Alatrista-Salas ; Denisse Muñante. Springer Verlag, 2019. pp. 273-288 (Communications in Computer and Information Science).

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Suni Lopez, F, Condori-Fernandez, N & Catala, A 2019, Towards real-time automatic stress detection for office workplaces. in JA Lossio-Ventura, H Alatrista-Salas & D Muñante (eds), Information Management and Big Data: 5th International Conference, SIMBig 2018, Lima, Peru, September 3 – 5, 2018, Proceedings. Communications in Computer and Information Science, vol. 898, Springer Verlag, pp. 273-288, 5th International Conference on Information Management and Big Data, SIMBig 2018, Lima, Peru, 3/09/18. https://doi.org/10.1007/978-3-030-11680-4_27

Towards real-time automatic stress detection for office workplaces. / Suni Lopez, Franci; Condori-Fernandez, Nelly; Catala, Alejandro.
Information Management and Big Data: 5th International Conference, SIMBig 2018, Lima, Peru, September 3 – 5, 2018, Proceedings. ed. / Juan Antonio Lossio-Ventura; Hugo Alatrista-Salas; Denisse Muñante. Springer Verlag, 2019. p. 273-288 (Communications in Computer and Information Science; Vol. 898).

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

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AU - Suni Lopez, Franci

AU - Condori-Fernandez, Nelly

AU - Catala, Alejandro

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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.

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Suni Lopez F, Condori-Fernandez N, Catala A. Towards real-time automatic stress detection for office workplaces. In Lossio-Ventura JA, Alatrista-Salas H, Muñante D, editors, Information Management and Big Data: 5th International Conference, SIMBig 2018, Lima, Peru, September 3 – 5, 2018, Proceedings. Springer Verlag. 2019. p. 273-288. (Communications in Computer and Information Science). Epub 2019 Feb 8. doi: 10.1007/978-3-030-11680-4_27

Towards real-time automatic stress detection for office workplaces

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Keywords

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