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

1 Citation (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 languageEnglish
Title of host publicationAmbient Intelligence - 15th European Conference, AmI 2019, Proceedings
EditorsIoannis Chatzigiannakis, Boris De Ruyter, Irene Mavrommati
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

Keywords

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

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  • 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. Lecture Notes in Computer Science, Vol.. 11912 https://doi.org/10.1007/978-3-030-34255-5_3