{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,30]],"date-time":"2025-08-30T16:41:57Z","timestamp":1756572117652},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643682860","type":"print"},{"value":"9781643682877","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,6,14]],"date-time":"2022-06-14T00:00:00Z","timestamp":1655164800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,6,14]]},"abstract":"<jats:p>Thermal comfort is a state of mind in which one is satisfied with the thermal environment that is crucial to human well-being, safety, and productivity in everyday life. Indoor environmental thermal comfort levels usually change due to performing different activities in different situations. Computer systems that can understand these comfort indicators can help to support and increase human wellbeing. This paper considers a simple wristwatch-like device equipped with various sensors to collect autonomic nervous system activity data. This study offers a preliminary assessment of a physiologically regulated thermal comfort provision based on Pulse Rate Variability (PRV) to see if we could predict the comfort of a hot environment (risk of heatstroke, higher dissatisfaction\/more difficult to cope than cold). Therefore, we focus on collecting data in varying temperatures and humidity levels for different work conditions i.e., reading, typewriting, and gymnastics focusing on hot thermal conditions to predict human-environmental thermal comfort using multiple machine learning models. Our results show an average accuracy above 95% with five different machine learning models.<\/jats:p>","DOI":"10.3233\/aise220058","type":"book-chapter","created":{"date-parts":[[2022,6,16]],"date-time":"2022-06-16T07:24:17Z","timestamp":1655364257000},"source":"Crossref","is-referenced-by-count":1,"title":["Toward the Prediction of Environmental Thermal Comfort Sensation Using Wearables"],"prefix":"10.3233","author":[{"given":"Tahera","family":"Hossain","sequence":"first","affiliation":[{"name":"Department of Integrated Information Technology, Aoyama Gakuin University, Kanagawa, Japan"}]},{"given":"Kizito","family":"Nkurikiyeyezu","sequence":"additional","affiliation":[{"name":"University of Rwanda, Kigali, Rwanda"}]},{"given":"Yusuke","family":"Kawasaki","sequence":"additional","affiliation":[{"name":"Department of Integrated Information Technology, Aoyama Gakuin University, Kanagawa, Japan"}]},{"given":"Guillaume","family":"Lopez","sequence":"additional","affiliation":[{"name":"Department of Integrated Information Technology, Aoyama Gakuin University, Kanagawa, Japan"}]}],"member":"7437","container-title":["Ambient Intelligence and Smart Environments","Workshops at 18th International Conference on Intelligent Environments (IE2022)"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/AISE220058","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,16]],"date-time":"2022-06-16T07:24:18Z","timestamp":1655364258000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/AISE220058"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,14]]},"ISBN":["9781643682860","9781643682877"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/aise220058","relation":{},"ISSN":["1875-4163","1875-4171"],"issn-type":[{"value":"1875-4163","type":"print"},{"value":"1875-4171","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,14]]}}}