Assessment of perceived indoor environmental quality, stress and productivity based on environmental sensor data and personality categorization
Indoor environmental quality (IEQ) has an influence on peoples' health, cognitive performance and productivity in school and office environments. This study used environmental temperature, humidity, air pressure, and CO2 sensor data collected during 3.5–7 months in an office and a school facili...
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Veröffentlicht in: | Building and environment 2020-05, Vol.175, p.106787, Article 106787 |
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creator | Kallio, Johanna Vildjiounaite, Elena Koivusaari, Jani Räsänen, Pauli Similä, Heidi Kyllönen, Vesa Muuraiskangas, Salla Ronkainen, Jussi Rehu, Jari Vehmas, Kaisa |
description | Indoor environmental quality (IEQ) has an influence on peoples' health, cognitive performance and productivity in school and office environments. This study used environmental temperature, humidity, air pressure, and CO2 sensor data collected during 3.5–7 months in an office and a school facility to classify occupants’ perceptions of IEQ, stress and productivity in two classes, “negative” and “positive”. Self-reported data from 15 office workers and four teachers were used to train person-specific SVM classifier models. Relatively high accuracies were achieved in classifying IEQ (84%), stress (88%) and productivity (92%) using different combinations of environmental sensor data. Furthermore, the associations between the Big Five personality trait variables (neuroticism, extraversion, openness, agreeableness and conscientiousness) and negative experiences regarding stress, productivity and IEQ were investigated. Positive correlation was found between extroversion and co-occurring stress and IEQ problems, which suggest that more extroverted people more likely to be stressed by insufficient environmental quality or to be more sensitive to environmental factors when under stress. Overall, the results indicate that it is possible to measure and classify perceived IEQ, stress and productivity sufficiently accurately using inexpensive environmental sensors.
•A method to use IEQ data to separate between positive and negative self-reports was developed.•Environmental sensor data can classify perceived IEQ (84%), stress (88%) and productivity (92%).•Comparison of sensors for analysis of IEQ, stress and productivity was provided.•More extroverted persons may express stress when environmental quality is insufficient. |
doi_str_mv | 10.1016/j.buildenv.2020.106787 |
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•A method to use IEQ data to separate between positive and negative self-reports was developed.•Environmental sensor data can classify perceived IEQ (84%), stress (88%) and productivity (92%).•Comparison of sensors for analysis of IEQ, stress and productivity was provided.•More extroverted persons may express stress when environmental quality is insufficient.</description><identifier>ISSN: 0360-1323</identifier><identifier>EISSN: 1873-684X</identifier><identifier>DOI: 10.1016/j.buildenv.2020.106787</identifier><language>eng</language><publisher>Oxford: Elsevier Ltd</publisher><subject>Air temperature ; Carbon dioxide ; Classification ; Cognitive ability ; Environmental assessment ; Environmental factors ; Environmental quality ; Extroversion ; Indoor environmental quality ; Indoor environments ; Machine learning ; Model accuracy ; Neurosis ; Personality ; Productivity ; Sensors ; Stress ; Support vector machines</subject><ispartof>Building and environment, 2020-05, Vol.175, p.106787, Article 106787</ispartof><rights>2020 The Author(s)</rights><rights>Copyright Elsevier BV May 15, 2020</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c454t-fef4ef029ebbb6cc91e6b9d5c53c6c2ca0b453890d0ccb2d0ef8f2013dec8e113</citedby><cites>FETCH-LOGICAL-c454t-fef4ef029ebbb6cc91e6b9d5c53c6c2ca0b453890d0ccb2d0ef8f2013dec8e113</cites><orcidid>0000-0002-7408-170X ; 0000-0003-4922-3443</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.buildenv.2020.106787$$EHTML$$P50$$Gelsevier$$Hfree_for_read</linktohtml><link.rule.ids>314,778,782,3539,27907,27908,45978</link.rule.ids></links><search><creatorcontrib>Kallio, Johanna</creatorcontrib><creatorcontrib>Vildjiounaite, Elena</creatorcontrib><creatorcontrib>Koivusaari, Jani</creatorcontrib><creatorcontrib>Räsänen, Pauli</creatorcontrib><creatorcontrib>Similä, Heidi</creatorcontrib><creatorcontrib>Kyllönen, Vesa</creatorcontrib><creatorcontrib>Muuraiskangas, Salla</creatorcontrib><creatorcontrib>Ronkainen, Jussi</creatorcontrib><creatorcontrib>Rehu, Jari</creatorcontrib><creatorcontrib>Vehmas, Kaisa</creatorcontrib><title>Assessment of perceived indoor environmental quality, stress and productivity based on environmental sensor data and personality categorization</title><title>Building and environment</title><description>Indoor environmental quality (IEQ) has an influence on peoples' health, cognitive performance and productivity in school and office environments. This study used environmental temperature, humidity, air pressure, and CO2 sensor data collected during 3.5–7 months in an office and a school facility to classify occupants’ perceptions of IEQ, stress and productivity in two classes, “negative” and “positive”. Self-reported data from 15 office workers and four teachers were used to train person-specific SVM classifier models. Relatively high accuracies were achieved in classifying IEQ (84%), stress (88%) and productivity (92%) using different combinations of environmental sensor data. Furthermore, the associations between the Big Five personality trait variables (neuroticism, extraversion, openness, agreeableness and conscientiousness) and negative experiences regarding stress, productivity and IEQ were investigated. Positive correlation was found between extroversion and co-occurring stress and IEQ problems, which suggest that more extroverted people more likely to be stressed by insufficient environmental quality or to be more sensitive to environmental factors when under stress. Overall, the results indicate that it is possible to measure and classify perceived IEQ, stress and productivity sufficiently accurately using inexpensive environmental sensors.
•A method to use IEQ data to separate between positive and negative self-reports was developed.•Environmental sensor data can classify perceived IEQ (84%), stress (88%) and productivity (92%).•Comparison of sensors for analysis of IEQ, stress and productivity was provided.•More extroverted persons may express stress when environmental quality is insufficient.</description><subject>Air temperature</subject><subject>Carbon dioxide</subject><subject>Classification</subject><subject>Cognitive ability</subject><subject>Environmental assessment</subject><subject>Environmental factors</subject><subject>Environmental quality</subject><subject>Extroversion</subject><subject>Indoor environmental quality</subject><subject>Indoor environments</subject><subject>Machine learning</subject><subject>Model accuracy</subject><subject>Neurosis</subject><subject>Personality</subject><subject>Productivity</subject><subject>Sensors</subject><subject>Stress</subject><subject>Support vector machines</subject><issn>0360-1323</issn><issn>1873-684X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNqFkMFqGzEQhkVpIK6TVwiCXLuutFrLu7eG0KQFQy4t5Ca0o9kgY0uORmtIXyKvHG23vfSS08DMfB8zP2NXUqykkPrLbtWPfu8wnFa1qKem3rSbD2wh242qdNs8fmQLobSopKrVOftEtBMF7FSzYK83REh0wJB5HPgRE6A_oeM-uBgTL1afYpjmds-fR7v3-eUzp5wKxW1w_JiiGyH7Uxnw3lJhY_iPIwxUZM5mOzOYKIY_Lg4241NM_rfNPoYLdjbYPeHl37pkv-6-_bz9Xm0f7n_c3mwraNZNrgYcGhxE3WHf9xqgk6j7zq1hrUBDDVb0zVq1nXACoK-dwKEdaiGVQ2hRSrVk17O3nP88ImWzi2MqJ5Gpm0aITm_0tKXnLUiRKOFgjskfbHoxUpgpfLMz_8I3U_hmDr-AX2cQyw8nj8kQeAyAzieEbFz07yneAB79lzs</recordid><startdate>20200515</startdate><enddate>20200515</enddate><creator>Kallio, Johanna</creator><creator>Vildjiounaite, Elena</creator><creator>Koivusaari, Jani</creator><creator>Räsänen, Pauli</creator><creator>Similä, Heidi</creator><creator>Kyllönen, Vesa</creator><creator>Muuraiskangas, Salla</creator><creator>Ronkainen, Jussi</creator><creator>Rehu, Jari</creator><creator>Vehmas, Kaisa</creator><general>Elsevier Ltd</general><general>Elsevier BV</general><scope>6I.</scope><scope>AAFTH</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7ST</scope><scope>8FD</scope><scope>C1K</scope><scope>F28</scope><scope>FR3</scope><scope>KR7</scope><scope>SOI</scope><orcidid>https://orcid.org/0000-0002-7408-170X</orcidid><orcidid>https://orcid.org/0000-0003-4922-3443</orcidid></search><sort><creationdate>20200515</creationdate><title>Assessment of perceived indoor environmental quality, stress and productivity based on environmental sensor data and personality categorization</title><author>Kallio, Johanna ; Vildjiounaite, Elena ; Koivusaari, Jani ; Räsänen, Pauli ; Similä, Heidi ; Kyllönen, Vesa ; Muuraiskangas, Salla ; Ronkainen, Jussi ; Rehu, Jari ; Vehmas, Kaisa</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c454t-fef4ef029ebbb6cc91e6b9d5c53c6c2ca0b453890d0ccb2d0ef8f2013dec8e113</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Air temperature</topic><topic>Carbon dioxide</topic><topic>Classification</topic><topic>Cognitive ability</topic><topic>Environmental assessment</topic><topic>Environmental factors</topic><topic>Environmental quality</topic><topic>Extroversion</topic><topic>Indoor environmental quality</topic><topic>Indoor environments</topic><topic>Machine learning</topic><topic>Model accuracy</topic><topic>Neurosis</topic><topic>Personality</topic><topic>Productivity</topic><topic>Sensors</topic><topic>Stress</topic><topic>Support vector machines</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kallio, Johanna</creatorcontrib><creatorcontrib>Vildjiounaite, Elena</creatorcontrib><creatorcontrib>Koivusaari, Jani</creatorcontrib><creatorcontrib>Räsänen, Pauli</creatorcontrib><creatorcontrib>Similä, Heidi</creatorcontrib><creatorcontrib>Kyllönen, Vesa</creatorcontrib><creatorcontrib>Muuraiskangas, Salla</creatorcontrib><creatorcontrib>Ronkainen, Jussi</creatorcontrib><creatorcontrib>Rehu, Jari</creatorcontrib><creatorcontrib>Vehmas, Kaisa</creatorcontrib><collection>ScienceDirect Open Access Titles</collection><collection>Elsevier:ScienceDirect:Open Access</collection><collection>CrossRef</collection><collection>Environment Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Civil Engineering Abstracts</collection><collection>Environment Abstracts</collection><jtitle>Building and environment</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Kallio, Johanna</au><au>Vildjiounaite, Elena</au><au>Koivusaari, Jani</au><au>Räsänen, Pauli</au><au>Similä, Heidi</au><au>Kyllönen, Vesa</au><au>Muuraiskangas, Salla</au><au>Ronkainen, Jussi</au><au>Rehu, Jari</au><au>Vehmas, Kaisa</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Assessment of perceived indoor environmental quality, stress and productivity based on environmental sensor data and personality categorization</atitle><jtitle>Building and environment</jtitle><date>2020-05-15</date><risdate>2020</risdate><volume>175</volume><spage>106787</spage><pages>106787-</pages><artnum>106787</artnum><issn>0360-1323</issn><eissn>1873-684X</eissn><abstract>Indoor environmental quality (IEQ) has an influence on peoples' health, cognitive performance and productivity in school and office environments. This study used environmental temperature, humidity, air pressure, and CO2 sensor data collected during 3.5–7 months in an office and a school facility to classify occupants’ perceptions of IEQ, stress and productivity in two classes, “negative” and “positive”. Self-reported data from 15 office workers and four teachers were used to train person-specific SVM classifier models. Relatively high accuracies were achieved in classifying IEQ (84%), stress (88%) and productivity (92%) using different combinations of environmental sensor data. Furthermore, the associations between the Big Five personality trait variables (neuroticism, extraversion, openness, agreeableness and conscientiousness) and negative experiences regarding stress, productivity and IEQ were investigated. Positive correlation was found between extroversion and co-occurring stress and IEQ problems, which suggest that more extroverted people more likely to be stressed by insufficient environmental quality or to be more sensitive to environmental factors when under stress. Overall, the results indicate that it is possible to measure and classify perceived IEQ, stress and productivity sufficiently accurately using inexpensive environmental sensors.
•A method to use IEQ data to separate between positive and negative self-reports was developed.•Environmental sensor data can classify perceived IEQ (84%), stress (88%) and productivity (92%).•Comparison of sensors for analysis of IEQ, stress and productivity was provided.•More extroverted persons may express stress when environmental quality is insufficient.</abstract><cop>Oxford</cop><pub>Elsevier Ltd</pub><doi>10.1016/j.buildenv.2020.106787</doi><orcidid>https://orcid.org/0000-0002-7408-170X</orcidid><orcidid>https://orcid.org/0000-0003-4922-3443</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Air temperature Carbon dioxide Classification Cognitive ability Environmental assessment Environmental factors Environmental quality Extroversion Indoor environmental quality Indoor environments Machine learning Model accuracy Neurosis Personality Productivity Sensors Stress Support vector machines |
title | Assessment of perceived indoor environmental quality, stress and productivity based on environmental sensor data and personality categorization |
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