Abstract
| Original language | English |
|---|---|
| Title of host publication | Ambient Intelligence. AmI 2019 |
| Publisher | Springer |
| Pages | 27-40 |
| ISBN (Electronic) | 978-3-030-34255-5 |
| ISBN (Print) | 978-3-030-34254-8 |
| DOIs | |
| 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 | Italy |
| City | Rome |
| Period | 12/11/19 → 15/11/19 |
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Keywords
- indoor environment quality
- Machine learning
- Classification
- Productivity
- Stress
- self-report
Cite this
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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 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
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
DO - https://doi.org/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
EP - 40
BT - Ambient Intelligence. AmI 2019
PB - Springer
ER -