@article{oai:ipsj.ixsq.nii.ac.jp:00225942,
 author = {Haomin, Mao and Shuhei, Tsuchida and Yuma, Suzuki and Rintaro, Kanada and Takayuki, Hori and Tsutomu, Terada and Masahiko, Tsukamoto and Haomin, Mao and Shuhei, Tsuchida and Yuma, Suzuki and Rintaro, Kanada and Takayuki, Hori and Tsutomu, Terada and Masahiko, Tsukamoto},
 issue = {5},
 journal = {情報処理学会論文誌},
 month = {May},
 note = {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
------------------------------, 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
------------------------------},
 title = {Subjective and Objective Thermal Comfort Estimation Using Wearable Sensors and Environmental Sensors},
 volume = {64},
 year = {2023}
}