{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00225942","sets":["581:11107:11114"]},"path":["11114"],"owner":"44499","recid":"225942","title":["Subjective and Objective Thermal Comfort Estimation Using Wearable Sensors and Environmental Sensors"],"pubdate":{"attribute_name":"公開日","attribute_value":"2023-05-15"},"_buckets":{"deposit":"7db580b5-9eec-4d77-8d90-da5bb10b49da"},"_deposit":{"id":"225942","pid":{"type":"depid","value":"225942","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"Subjective and Objective Thermal Comfort Estimation Using Wearable Sensors and Environmental Sensors","author_link":["599057","599054","599048","599049","599056","599058","599050","599052","599053","599046","599047","599055","599051","599059"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Subjective and Objective Thermal Comfort Estimation Using Wearable Sensors and Environmental Sensors"},{"subitem_title":"Subjective and Objective Thermal Comfort Estimation Using Wearable Sensors and Environmental Sensors","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"[一般論文] Thermal Comfort, PMV, Wearable Sensor, Machine Learning","subitem_subject_scheme":"Other"}]},"item_type_id":"2","publish_date":"2023-05-15","item_2_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Engineering, Kobe University"},{"subitem_text_value":"Graduate School of Engineering, Kobe University"},{"subitem_text_value":"SoftBank Corp."},{"subitem_text_value":"SoftBank Corp."},{"subitem_text_value":"SoftBank Corp."},{"subitem_text_value":"Graduate School of Engineering, Kobe University"},{"subitem_text_value":"Graduate School of Engineering, Kobe University"}]},"item_2_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Engineering, Kobe University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Engineering, Kobe University","subitem_text_language":"en"},{"subitem_text_value":"SoftBank Corp.","subitem_text_language":"en"},{"subitem_text_value":"SoftBank Corp.","subitem_text_language":"en"},{"subitem_text_value":"SoftBank Corp.","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Engineering, Kobe University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Engineering, Kobe University","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing 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Mao"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Shuhei, Tsuchida"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Yuma, Suzuki"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Rintaro, Kanada"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Takayuki, Hori"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Tsutomu, Terada"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Masahiko, Tsukamoto"}],"nameIdentifiers":[{}]}]},"item_2_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Haomin, Mao","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Shuhei, Tsuchida","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Yuma, Suzuki","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Rintaro, Kanada","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Takayuki, Hori","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Tsutomu, Terada","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Masahiko, Tsukamoto","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_2_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN00116647","subitem_source_identifier_type":"NCID"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_6501","resourcetype":"journal article"}]},"item_2_publisher_15":{"attribute_name":"公開者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"item_2_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"1882-7764","subitem_source_identifier_type":"ISSN"}]},"item_2_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"An ideal self-adjusting environment requires adapting human thermal comfort automatically and continuously measuring the changes in human thermal comfort. According to the PMV (Predicted Mean Vote) model, human thermal comfort could be evaluated by human biometric data and environmental data. In this paper, we proposed a method to estimate human thermal comfort through a small number of wearable and environmental sensors based on machine learning. There are two typical definitions of thermal comfort: subjective thermal comfort representing the subjective perception of heat and objective thermal comfort calculated by the PMV formula. We used a subjective questionnaire and PMV formula to obtain the correct label for two kinds of thermal comfort, respectively. Among the three machine learning models, the random forest has 0.73 in MAE, which is suitable for estimating 7-level subjective thermal comfort, and the neural network has 0.47 in MAE, which is suitable for estimating objective thermal comfort. We investigated the estimation accuracy by changing the sensors' combinations. As a result, a small number of sensors could still roughly estimate human thermal comfort.\n------------------------------\nThis is a preprint of an article intended for publication Journal of\nInformation Processing(JIP). This preprint should not be cited. This\narticle should be cited as: Journal of Information Processing Vol.31(2023) (online)\nDOI　http://dx.doi.org/10.2197/ipsjjip.31.308\n------------------------------","subitem_description_type":"Other"}]},"item_2_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"An ideal self-adjusting environment requires adapting human thermal comfort automatically and continuously measuring the changes in human thermal comfort. According to the PMV (Predicted Mean Vote) model, human thermal comfort could be evaluated by human biometric data and environmental data. In this paper, we proposed a method to estimate human thermal comfort through a small number of wearable and environmental sensors based on machine learning. There are two typical definitions of thermal comfort: subjective thermal comfort representing the subjective perception of heat and objective thermal comfort calculated by the PMV formula. We used a subjective questionnaire and PMV formula to obtain the correct label for two kinds of thermal comfort, respectively. Among the three machine learning models, the random forest has 0.73 in MAE, which is suitable for estimating 7-level subjective thermal comfort, and the neural network has 0.47 in MAE, which is suitable for estimating objective thermal comfort. We investigated the estimation accuracy by changing the sensors' combinations. As a result, a small number of sensors could still roughly estimate human thermal comfort.\n------------------------------\nThis is a preprint of an article intended for publication Journal of\nInformation Processing(JIP). This preprint should not be cited. This\narticle should be cited as: Journal of Information Processing Vol.31(2023) (online)\nDOI　http://dx.doi.org/10.2197/ipsjjip.31.308\n------------------------------","subitem_description_type":"Other"}]},"item_2_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographic_titles":[{"bibliographic_title":"情報処理学会論文誌"}],"bibliographicIssueDates":{"bibliographicIssueDate":"2023-05-15","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"5","bibliographicVolumeNumber":"64"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":225942,"updated":"2025-01-19T12:38:06.849713+00:00","links":{},"created":"2025-01-19T01:25:26.108150+00:00"}