Classifying Teachers’ Self-reported Productivity, Stress and Indoor Environmental Quality Using Environmental Sensors

Johanna Kallio; Elena Vildjiounaite; Vesa Kyllönen; Jussi Ronkainen; Jani Koivusaari; Salla Muuraiskangas; Pauli Räsänen; Heidi Similä; Kaisa Vehmas

doi:10.1007/978-3-030-34255-5_3

Classifying Teachers’ Self-reported Productivity, Stress and Indoor Environmental Quality Using Environmental Sensors

Johanna Kallio^*, Elena Vildjiounaite, Vesa Kyllönen, Jussi Ronkainen, Jani Koivusaari, Salla Muuraiskangas, Pauli Räsänen, Heidi Similä, Kaisa Vehmas

^*Corresponding author for this work

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

2 Citations (Scopus)

Abstract

Considering that urban people spend majority of their time indoors, buildings should support health and productivity. However, quite commonly unsatisfactory Indoor Environmental Quality (IEQ) causes environmental stress, which can lead to adverse health outcomes and reduced productivity. First step to enable automatic environmental control is to recognise environmental conditions that can negatively influence each individual. To this end, we developed (1) multi-sensor IEQ monitoring system to measure objectively environmental quality; (2) mobile application to collect subjective evaluation of productivity, stress and IEQ data; (3) machine learning method to use IEQ data to distinguish between positive and negative self-reports of test subjects. Experimental results with real life data, collected in four classrooms of Finnish elementary school during 18 weeks, show that IEQ sensor data allows to classify with fairly high accuracy perceptions of teachers regarding their work productivity (91%), stress (81%) and IEQ (92%). This result was achieved in person-specific training (i.e., model of each individual was trained using only his/her data), whereas accuracy of leave-one-person-out approach was notably lower. These results suggest that perception is personal and some individuals are more sensitive to environmental stressors.

Original language	English
Title of host publication	Ambient Intelligence - 15th European Conference, AmI 2019, Proceedings
Editors	Ioannis Chatzigiannakis, Boris De Ruyter, Irene Mavrommati
Publisher	Springer
Pages	27-40
ISBN (Electronic)	978-3-030-34255-5
ISBN (Print)	978-3-030-34254-8
DOIs	https://doi.org/10.1007/978-3-030-34255-5_3
Publication status	Published - Nov 2019
MoE publication type	A4 Article in a conference publication
Event	European Conference on Ambient Intelligence, Aml 2019 - Rome, Italy Duration: 12 Nov 2019 → 15 Nov 2019

Publication series

Series	Lecture Notes in Computer Science
Volume	11912
ISSN	0302-9743

Conference

Conference	European Conference on Ambient Intelligence, Aml 2019
Abbreviated title	Aml 2019
Country/Territory	Italy
City	Rome
Period	12/11/19 → 15/11/19

Funding

The study was implemented in collaboration with ESTABLISH and SCOTT projects. ESTABLISH (ITEA3 15008) was supported by Business Finland and VTT Technical Research Centre of Finland, and the pilot was designed in collaboration with UniqAir, CGI, and InspectorSec. SCOTT (Secure COnnected Trustable Things) has received funding from the Electronic Component Systems for European Leadership Joint Undertaking under grant agreement No 737422. This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme, and from Austria, Spain, Finland, Ireland, Sweden, Germany, Poland, Portugal, Netherlands, Belgium and Norway.

Keywords

indoor environment quality
Machine learning
Classification
Productivity
Stress
self-report

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-030-34255-5_3

Cite this

Kallio, J., Vildjiounaite, E., Kyllönen, V., Ronkainen, J., Koivusaari, J., Muuraiskangas, S., Räsänen, P., Similä, H., & Vehmas, K. (2019). Classifying Teachers’ Self-reported Productivity, Stress and Indoor Environmental Quality Using Environmental Sensors. In I. Chatzigiannakis, B. De Ruyter, & I. Mavrommati (Eds.), Ambient Intelligence - 15th European Conference, AmI 2019, Proceedings (pp. 27-40). Springer. https://doi.org/10.1007/978-3-030-34255-5_3

Kallio, Johanna ; Vildjiounaite, Elena ; Kyllönen, Vesa et al. / Classifying Teachers’ Self-reported Productivity, Stress and Indoor Environmental Quality Using Environmental Sensors. Ambient Intelligence - 15th European Conference, AmI 2019, Proceedings. editor / Ioannis Chatzigiannakis ; Boris De Ruyter ; Irene Mavrommati. Springer, 2019. pp. 27-40 (Lecture Notes in Computer Science, Vol. 11912).

@inproceedings{b6052c1a89b14ab196894f5982aff659,

title = "Classifying Teachers{\textquoteright} Self-reported Productivity, Stress and Indoor Environmental Quality Using Environmental Sensors",

abstract = "Considering that urban people spend majority of their time indoors, buildings should support health and productivity. However, quite commonly unsatisfactory Indoor Environmental Quality (IEQ) causes environmental stress, which can lead to adverse health outcomes and reduced productivity. First step to enable automatic environmental control is to recognise environmental conditions that can negatively influence each individual. To this end, we developed (1) multi-sensor IEQ monitoring system to measure objectively environmental quality; (2) mobile application to collect subjective evaluation of productivity, stress and IEQ data; (3) machine learning method to use IEQ data to distinguish between positive and negative self-reports of test subjects. Experimental results with real life data, collected in four classrooms of Finnish elementary school during 18 weeks, show that IEQ sensor data allows to classify with fairly high accuracy perceptions of teachers regarding their work productivity (91%), stress (81%) and IEQ (92%). This result was achieved in person-specific training (i.e., model of each individual was trained using only his/her data), whereas accuracy of leave-one-person-out approach was notably lower. These results suggest that perception is personal and some individuals are more sensitive to environmental stressors.",

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author = "Johanna Kallio and Elena Vildjiounaite and Vesa Kyll{\"o}nen and Jussi Ronkainen and Jani Koivusaari and Salla Muuraiskangas and Pauli R{\"a}s{\"a}nen and Heidi Simil{\"a} and Kaisa Vehmas",

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Kallio, J , Vildjiounaite, E , Kyllönen, V , Ronkainen, J, Koivusaari, J, Muuraiskangas, S, Räsänen, P, Similä, H & Vehmas, K 2019, Classifying Teachers’ Self-reported Productivity, Stress and Indoor Environmental Quality Using Environmental Sensors. in I Chatzigiannakis, B De Ruyter & I Mavrommati (eds), Ambient Intelligence - 15th European Conference, AmI 2019, Proceedings. Springer, Lecture Notes in Computer Science, vol. 11912, pp. 27-40, European Conference on Ambient Intelligence, Aml 2019, Rome, Italy, 12/11/19. https://doi.org/10.1007/978-3-030-34255-5_3

Classifying Teachers’ Self-reported Productivity, Stress and Indoor Environmental Quality Using Environmental Sensors. / Kallio, Johanna ; Vildjiounaite, Elena ; Kyllönen, Vesa et al.
Ambient Intelligence - 15th European Conference, AmI 2019, Proceedings. ed. / Ioannis Chatzigiannakis; Boris De Ruyter; Irene Mavrommati. Springer, 2019. p. 27-40 (Lecture Notes in Computer Science, Vol. 11912).

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

TY - GEN

T1 - Classifying Teachers’ Self-reported Productivity, Stress and Indoor Environmental Quality Using Environmental Sensors

AU - Kallio, Johanna

AU - Vildjiounaite, Elena

AU - Kyllönen, Vesa

AU - Ronkainen, Jussi

AU - Koivusaari, Jani

AU - Muuraiskangas, Salla

AU - Räsänen, Pauli

AU - Similä, Heidi

AU - Vehmas, Kaisa

N1 - Publisher Copyright: © Springer Nature Switzerland AG 2019.

PY - 2019/11

Y1 - 2019/11

N2 - Considering that urban people spend majority of their time indoors, buildings should support health and productivity. However, quite commonly unsatisfactory Indoor Environmental Quality (IEQ) causes environmental stress, which can lead to adverse health outcomes and reduced productivity. First step to enable automatic environmental control is to recognise environmental conditions that can negatively influence each individual. To this end, we developed (1) multi-sensor IEQ monitoring system to measure objectively environmental quality; (2) mobile application to collect subjective evaluation of productivity, stress and IEQ data; (3) machine learning method to use IEQ data to distinguish between positive and negative self-reports of test subjects. Experimental results with real life data, collected in four classrooms of Finnish elementary school during 18 weeks, show that IEQ sensor data allows to classify with fairly high accuracy perceptions of teachers regarding their work productivity (91%), stress (81%) and IEQ (92%). This result was achieved in person-specific training (i.e., model of each individual was trained using only his/her data), whereas accuracy of leave-one-person-out approach was notably lower. These results suggest that perception is personal and some individuals are more sensitive to environmental stressors.

AB - Considering that urban people spend majority of their time indoors, buildings should support health and productivity. However, quite commonly unsatisfactory Indoor Environmental Quality (IEQ) causes environmental stress, which can lead to adverse health outcomes and reduced productivity. First step to enable automatic environmental control is to recognise environmental conditions that can negatively influence each individual. To this end, we developed (1) multi-sensor IEQ monitoring system to measure objectively environmental quality; (2) mobile application to collect subjective evaluation of productivity, stress and IEQ data; (3) machine learning method to use IEQ data to distinguish between positive and negative self-reports of test subjects. Experimental results with real life data, collected in four classrooms of Finnish elementary school during 18 weeks, show that IEQ sensor data allows to classify with fairly high accuracy perceptions of teachers regarding their work productivity (91%), stress (81%) and IEQ (92%). This result was achieved in person-specific training (i.e., model of each individual was trained using only his/her data), whereas accuracy of leave-one-person-out approach was notably lower. These results suggest that perception is personal and some individuals are more sensitive to environmental stressors.

KW - indoor environment quality

KW - Machine learning

KW - Classification

KW - Productivity

KW - Stress

KW - self-report

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DO - 10.1007/978-3-030-34255-5_3

M3 - Conference article in proceedings

SN - 978-3-030-34254-8

T3 - Lecture Notes in Computer Science

SP - 27

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BT - Ambient Intelligence - 15th European Conference, AmI 2019, Proceedings

A2 - Chatzigiannakis, Ioannis

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Kallio J , Vildjiounaite E , Kyllönen V , Ronkainen J, Koivusaari J, Muuraiskangas S et al. Classifying Teachers’ Self-reported Productivity, Stress and Indoor Environmental Quality Using Environmental Sensors. In Chatzigiannakis I, De Ruyter B, Mavrommati I, editors, Ambient Intelligence - 15th European Conference, AmI 2019, Proceedings. Springer. 2019. p. 27-40. (Lecture Notes in Computer Science, Vol. 11912). doi: 10.1007/978-3-030-34255-5_3

Classifying Teachers’ Self-reported Productivity, Stress and Indoor Environmental Quality Using Environmental Sensors

Abstract

Publication series

Conference

Funding

Keywords

UN SDGs

Access to Document

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