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

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

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 languageEnglish
Title of host publicationAmbient Intelligence. AmI 2019
PublisherSpringer
Pages27-40
ISBN (Electronic)978-3-030-34255-5
ISBN (Print)978-3-030-34254-8
DOIs
Publication statusPublished - Nov 2019
MoE publication typeA4 Article in a conference publication
EventEuropean Conference on Ambient Intelligence, Aml 2019 - Rome, Italy
Duration: 12 Nov 201915 Nov 2019

Publication series

SeriesLecture Notes in Computer Science
Volume11912
ISSN0302-9743

Conference

ConferenceEuropean Conference on Ambient Intelligence, Aml 2019
Abbreviated titleAml 2019
CountryItaly
CityRome
Period12/11/1915/11/19

Fingerprint

Productivity
Sensors
Health
Learning systems
Monitoring

Keywords

  • indoor environment quality
  • Machine learning
  • Classification
  • Productivity
  • Stress
  • self-report

Cite this

@inproceedings{b6052c1a89b14ab196894f5982aff659,
title = "Classifying Teachers’ 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.",
keywords = "indoor environment quality, Machine learning, Classification, Productivity, Stress, self-report",
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",
year = "2019",
month = "11",
doi = "https://doi.org/10.1007/978-3-030-34255-5_3",
language = "English",
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series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "27--40",
booktitle = "Ambient Intelligence. AmI 2019",
address = "Germany",

}

Classifying Teachers’ Self-reported Productivity, Stress and Indoor Environmental Quality Using Environmental Sensors. / Kallio, Johanna (Corresponding author); Vildjiounaite, Elena; Kyllönen, Vesa; Ronkainen, Jussi; Koivusaari, Jani; Muuraiskangas, Salla; Räsänen, Pauli; Similä, Heidi; Vehmas, Kaisa.

Ambient Intelligence. AmI 2019. Springer, 2019. p. 27-40 (Lecture Notes in Computer Science, Vol. 11912).

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-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

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

U2 - https://doi.org/10.1007/978-3-030-34255-5_3

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T3 - Lecture Notes in Computer Science

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