Subjective and Objective Thermal Comfort Estimation Using Wearable Sensors and Environmental Sensors

• Item type: Journal(1)
• 公開日: 2023-05-15
• タイトル: Subjective and Objective Thermal Comfort Estimation Using Wearable Sensors and Environmental Sensors
• タイトル / 言語: en
• タイトル: Subjective and Objective Thermal Comfort Estimation Using Wearable Sensors and Environmental Sensors
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: [一般論文] Thermal Comfort, PMV, Wearable Sensor, Machine Learning
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_6501
• 資源タイプ: journal article
• 著者所属: Graduate School of Engineering, Kobe University
• 著者所属: Graduate School of Engineering, Kobe University
• 著者所属: SoftBank Corp.
• 著者所属: SoftBank Corp.
• 著者所属: SoftBank Corp.
• 著者所属: Graduate School of Engineering, Kobe University
• 著者所属: Graduate School of Engineering, Kobe University
• 著者所属(英): en
• 著者所属(英): Graduate School of Engineering, Kobe University
• 著者所属(英): en
• 著者所属(英): Graduate School of Engineering, Kobe University
• 著者所属(英): en
• 著者所属(英): SoftBank Corp.
• 著者所属(英): en
• 著者所属(英): SoftBank Corp.
• 著者所属(英): en
• 著者所属(英): SoftBank Corp.
• 著者所属(英): en
• 著者所属(英): Graduate School of Engineering, Kobe University
• 著者所属(英): en
• 著者所属(英): Graduate School of Engineering, Kobe University
• 著者名: Haomin, Mao; Shuhei, Tsuchida; Yuma, Suzuki; Rintaro, Kanada; Takayuki, Hori; Tsutomu, Terada; Masahiko, Tsukamoto
• 著者名(英): Haomin, Mao; Shuhei, Tsuchida; Yuma, Suzuki; Rintaro, Kanada; Takayuki, Hori; Tsutomu, Terada; Masahiko, Tsukamoto
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: 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.; ------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.31(2023) (online); DOI http://dx.doi.org/10.2197/ipsjjip.31.308; ------------------------------
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: 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.; ------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.31(2023) (online); DOI http://dx.doi.org/10.2197/ipsjjip.31.308; ------------------------------
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AN00116647
• 書誌情報: 情報処理学会論文誌; 巻 64, 号 5, 発行日 2023-05-15
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-7764
• 公開者 / 言語: ja
• 公開者 / 出版者: 情報処理学会