Detection of prolonged stress in smart office

Elena Vildjiounaite; Ville Huotari; Johanna Kallio; Vesa Kyllönen; Satu Marja Mäkelä; Georgy Gimel’farb

doi:10.1007/978-3-030-01177-2_90

Detection of prolonged stress in smart office

Elena Vildjiounaite^*
, Ville Huotari
, Johanna Kallio
, Vesa Kyllönen
, Satu Marja Mäkelä
, Georgy Gimel’farb

^*Corresponding author for this work

The University of Auckland

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

5 Citations (Scopus)

Abstract

Detection of long-lasting stress in its early stage may allow prevention of irreversible health damages, but most of today’s stress detectors are not sufficiently convenient for long-term use. Some detectors rely on wearable physiological devices, known to have high abandonment rate, whereas other systems collect only behavioural data, but require each user to report occurrences of his/her stress during at least a month because behavioural patterns notably vary between individuals. Therefore, behaviour-based stress detection models should be learned separately for each person, and in existing systems such learning is typically supervised. In contrast, this work proposes a person-specific stress monitoring system, requiring no efforts from end users. This system acquires users’ motion trajectories via in-office depth cameras and employs a novel unsupervised method for analysis of these trajectories, based on discrete Hidden Markov Models. In 10-months-long real life study the proposed system correctly recognised the most stressful working periods of the monitored subjects and pointed out the most stress-prone persons.

Original language	English
Title of host publication	Intelligent Computing
Subtitle of host publication	Proceedings of the 2018 Computing Conference
Editors	Supriya Kapoor, Rahul Bhatia, Kohei Arai
Publisher	Springer
Pages	1253-1261
Number of pages	9
ISBN (Print)	978-303-00117-6-5
DOIs	https://doi.org/10.1007/978-3-030-01177-2_90
Publication status	Published - 2018
MoE publication type	Not Eligible
Event	Computing Conference 2018, SAI 2018 - London, United Kingdom Duration: 10 Jul 2018 → 12 Jul 2018

Publication series

Series	Advances in Intelligent Systems and Computing
Volume	857
ISSN	2194-5357

Conference

Conference	Computing Conference 2018, SAI 2018
Abbreviated title	SAI 2018
Country/Territory	United Kingdom
City	London
Period	10/07/18 → 12/07/18

Funding

This work was supported by the Finnish Funding Agency for Technology and Innovation (Tekes) and VTT Technical Research Centre of Finland (ITEA 3 15008 ESTABLISH project). Acknowledgment. This research has been conducted as a part of the ITEA 3 15008 ESTABLISH project. We thank test subjects for their efforts to provide stress labels during a long study.

Keywords

Ambient intelligence
Hidden Markov Models (HMM)
Stress detection
Unsupervised learning

Access to Document

10.1007/978-3-030-01177-2_90

Cite this

@inproceedings{c9ad2599cf4e4b31b6635075f8a0d5e6,

title = "Detection of prolonged stress in smart office",

abstract = "Detection of long-lasting stress in its early stage may allow prevention of irreversible health damages, but most of today{\textquoteright}s stress detectors are not sufficiently convenient for long-term use. Some detectors rely on wearable physiological devices, known to have high abandonment rate, whereas other systems collect only behavioural data, but require each user to report occurrences of his/her stress during at least a month because behavioural patterns notably vary between individuals. Therefore, behaviour-based stress detection models should be learned separately for each person, and in existing systems such learning is typically supervised. In contrast, this work proposes a person-specific stress monitoring system, requiring no efforts from end users. This system acquires users{\textquoteright} motion trajectories via in-office depth cameras and employs a novel unsupervised method for analysis of these trajectories, based on discrete Hidden Markov Models. In 10-months-long real life study the proposed system correctly recognised the most stressful working periods of the monitored subjects and pointed out the most stress-prone persons.",

keywords = "Ambient intelligence, Hidden Markov Models (HMM), Stress detection, Unsupervised learning",

author = "Elena Vildjiounaite and Ville Huotari and Johanna Kallio and Vesa Kyll{\"o}nen and M{\"a}kel{\"a}, \{Satu Marja\} and Georgy Gimel{\textquoteright}farb",

note = "Funding Information: This work was supported by the Finnish Funding Agency for Technology and Innovation (Tekes) and VTT Technical Research Centre of Finland (ITEA 3 15008 ESTABLISH project). Funding Information: This research has been conducted as a part of the ITEA 3 15008 ESTAB-LISH project. We thank test subjects for their efforts to provide stress labels during a long study. Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2019. Copyright: Copyright 2019 Elsevier B.V., All rights reserved.; Computing Conference 2018, SAI 2018, SAI 2018 ; Conference date: 10-07-2018 Through 12-07-2018",

year = "2018",

doi = "10.1007/978-3-030-01177-2\_90",

language = "English",

isbn = "978-303-00117-6-5",

series = "Advances in Intelligent Systems and Computing",

publisher = "Springer",

pages = "1253--1261",

editor = "Supriya Kapoor and Rahul Bhatia and Kohei Arai",

booktitle = "Intelligent Computing",

address = "Germany",

}

Vildjiounaite, E, Huotari, V, Kallio, J , Kyllönen, V , Mäkelä, SM & Gimel’farb, G 2018, Detection of prolonged stress in smart office. in S Kapoor, R Bhatia & K Arai (eds), Intelligent Computing: Proceedings of the 2018 Computing Conference. Springer, Advances in Intelligent Systems and Computing, vol. 857, pp. 1253-1261, Computing Conference 2018, SAI 2018, London, United Kingdom, 10/07/18. https://doi.org/10.1007/978-3-030-01177-2_90

Detection of prolonged stress in smart office. / Vildjiounaite, Elena; Huotari, Ville; Kallio, Johanna et al.
Intelligent Computing: Proceedings of the 2018 Computing Conference. ed. / Supriya Kapoor; Rahul Bhatia; Kohei Arai. Springer, 2018. p. 1253-1261 (Advances in Intelligent Systems and Computing, Vol. 857).

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

TY - GEN

T1 - Detection of prolonged stress in smart office

AU - Vildjiounaite, Elena

AU - Huotari, Ville

AU - Kallio, Johanna

AU - Kyllönen, Vesa

AU - Mäkelä, Satu Marja

AU - Gimel’farb, Georgy

N1 - Funding Information: This work was supported by the Finnish Funding Agency for Technology and Innovation (Tekes) and VTT Technical Research Centre of Finland (ITEA 3 15008 ESTABLISH project). Funding Information: This research has been conducted as a part of the ITEA 3 15008 ESTAB-LISH project. We thank test subjects for their efforts to provide stress labels during a long study. Publisher Copyright: © Springer Nature Switzerland AG 2019. Copyright: Copyright 2019 Elsevier B.V., All rights reserved.

PY - 2018

Y1 - 2018

N2 - Detection of long-lasting stress in its early stage may allow prevention of irreversible health damages, but most of today’s stress detectors are not sufficiently convenient for long-term use. Some detectors rely on wearable physiological devices, known to have high abandonment rate, whereas other systems collect only behavioural data, but require each user to report occurrences of his/her stress during at least a month because behavioural patterns notably vary between individuals. Therefore, behaviour-based stress detection models should be learned separately for each person, and in existing systems such learning is typically supervised. In contrast, this work proposes a person-specific stress monitoring system, requiring no efforts from end users. This system acquires users’ motion trajectories via in-office depth cameras and employs a novel unsupervised method for analysis of these trajectories, based on discrete Hidden Markov Models. In 10-months-long real life study the proposed system correctly recognised the most stressful working periods of the monitored subjects and pointed out the most stress-prone persons.

AB - Detection of long-lasting stress in its early stage may allow prevention of irreversible health damages, but most of today’s stress detectors are not sufficiently convenient for long-term use. Some detectors rely on wearable physiological devices, known to have high abandonment rate, whereas other systems collect only behavioural data, but require each user to report occurrences of his/her stress during at least a month because behavioural patterns notably vary between individuals. Therefore, behaviour-based stress detection models should be learned separately for each person, and in existing systems such learning is typically supervised. In contrast, this work proposes a person-specific stress monitoring system, requiring no efforts from end users. This system acquires users’ motion trajectories via in-office depth cameras and employs a novel unsupervised method for analysis of these trajectories, based on discrete Hidden Markov Models. In 10-months-long real life study the proposed system correctly recognised the most stressful working periods of the monitored subjects and pointed out the most stress-prone persons.

KW - Ambient intelligence

KW - Hidden Markov Models (HMM)

KW - Stress detection

KW - Unsupervised learning

UR - https://www.scopus.com/pages/publications/85057106645

U2 - 10.1007/978-3-030-01177-2_90

DO - 10.1007/978-3-030-01177-2_90

M3 - Conference article in proceedings

AN - SCOPUS:85057106645

SN - 978-303-00117-6-5

T3 - Advances in Intelligent Systems and Computing

SP - 1253

EP - 1261

BT - Intelligent Computing

A2 - Kapoor, Supriya

A2 - Bhatia, Rahul

A2 - Arai, Kohei

PB - Springer

T2 - Computing Conference 2018, SAI 2018

Y2 - 10 July 2018 through 12 July 2018

ER -

Vildjiounaite E, Huotari V, Kallio J , Kyllönen V , Mäkelä SM, Gimel’farb G. Detection of prolonged stress in smart office. In Kapoor S, Bhatia R, Arai K, editors, Intelligent Computing: Proceedings of the 2018 Computing Conference. Springer. 2018. p. 1253-1261. (Advances in Intelligent Systems and Computing, Vol. 857). Epub 2018 Nov 2. doi: 10.1007/978-3-030-01177-2_90

Detection of prolonged stress in smart office

Abstract

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Keywords

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