Estimation of thermal sensation using human peripheral skin temperature

Nakayama, Keiichi I.; Suzuki, Takuji; Kameyama, Ken-ichi

doi:10.1109/icsmc.2009.5346126

Cited by 16 publications

(13 citation statements)

References 5 publications

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“…Uniform and non-uniform, steady state and transient. Not validated Nakayama et al [28] Left index finger skin temperature Calculate the frequency and gradient of the skin temperature to evaluate thermal sensation.…”

Section: Resultsmentioning

confidence: 99%

Machine learning approaches to predict thermal demands using skin temperatures: Steady-state conditions

Dai

Zhang

Arens

et al. 2017

Building and Environment

134

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Inefficient controlling strategies in heating and cooling systems have given rise to a large amount of energy waste and to widespread complaints about the thermal environment in buildings. An intelligent control method based on a support vector machine (SVM) classifier is proposed in this paper. Skin temperatures are the only inputs to the model and have shown attractive prediction power in recognizing steady state thermal demands. Data were accumulated from two studies to consider potential use for either individuals or a group of occupants. Using a single skin temperature correctly predicts 80% of thermal demands. Using combined skin temperatures from different body segments can improve the model to over 90% accuracy. Results show that three skin locations contained enough information for classification and more would cause the curse of dimensionality. Models using different skin temperatures were compared. Optimal parameters for each model were provided using grid search technique. Considering the overfitting possibility and the cases without learning processes, SVM classifiers with a linear kernel are preferred over Gaussian kernel ones.

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Section: Resultsmentioning

confidence: 99%

Machine learning approaches to predict thermal demands using skin temperatures: Steady-state conditions

Dai

Zhang

Arens

et al. 2017

Building and Environment

134

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“…The validation results showed that a personalized thermal sensation estimation model based on three wrist skin temperatures would have the mean RMSE of 1.06 ± 0.29, with a correlation coefficient of 0.89. Authors in [27] developed an estimation algorithm for individual thermal sensation based on the peripheral skin temperature measurements. They used environmental temperatures modification to explore the relationship between peripheral skin temperature and thermal sensation votes.…”

Section: Physiological Sensing Of Thermal Comfortmentioning

confidence: 99%

Infrared thermography of human face for monitoring thermoregulation performance and estimating personal thermal comfort

Ghahramani

Castro

Becerik-Gerber

et al. 2016

Building and Environment

200

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The common practice of defining operational settings for Heating, Ventilation and Air Conditioning (HVAC) systems in buildings is to use fixed set points, which assume occupants have same and static comfort requirements. However, thermal comfort varies from person to person and also changes due to climatic variations or acclimation, making it dynamic. In addition, thermal comfort in transient conditions are different from the steady state conditions, which makes the prediction of thermal comfort more difficult. Thus, thermal comfort has to be monitored over time. In this paper, we present a novel infrared thermography based technique to monitor an individual's thermoregulation performance and thermal comfort levels by measuring the skin temperature on several points on human face, which has a high density of blood vessels and is not usually covered by clothing. Unlike other methods, our method requires no continuous user input or interaction. Our results demonstrate that the monitored facial points behave differently under the heat and cold stresses and it can be explained based on the underlying vascular territories. We define two heuristics to describe the thermoneutral zone based on the observed behaviors and estimate thermal comfort for individuals with 95% confidence level. Considerable variations are observed in the thermoregulation performance and uncomfortably cool conditions metrics between the males and females. Females' thermoregulation system responses are less sensitive to the perception of warm conditions. However, similar behaviors are observed for uncomfortably cool conditions across genders.

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“…Adapun faktor fisiologis yang menjadi parameter dalam mengontrol suhu ruangan antara lain adalah melihat suhu yang dihasilkan dari permukaan kulit dan rekaman detak jantung seseorang. Parameter tersebut diharapkan dapat memberikan informasi yang lebih baik untuk mengetahui kenyamanan seseorang dengan suhu yang ada di sekitar mereka [3], [4].…”

Section: Pendahuluanunclassified

Perbandingan Metode Klasifikasi untuk Menentukan Tingkat Kenyamanan Suhu pada Kondisi Rileks Berbasis Sinyal EEG

Saputra

Ratri

2019

Ultimatics

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Temperature control of air conditioner devices is still oriented to the target environment. This control mode ignores one's physiological condition. A person's thermal comfort varies when indoors. Thermal comfort is closely related to environmental thermal satisfaction conditions. EEG signal is a signal that can reflect brain activity. This research objective is to provide classifier model for classifying person's thermal comfort based on EEG signal. This research used three conditions of a room's temperature. The features used by classifier are an average frequency band, HFD, PFD, and MSE features. Classifier performance was assessed using ROC curve evaluation. The results of the classification of thermal comfort levels with EEG signals with the KNN classifier are obtained only by using the band frequency average feature, which is equal to 0.878 with a standard deviation of 0.022. While the SVM classifier gets the highest performance by using a combination of the average band + HFD frequency feature, which is 0.877 with a standard deviation of 0.013 in the linear kernel and RBF.

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Estimation of thermal sensation using human peripheral skin temperature

Cited by 16 publications

References 5 publications

Machine learning approaches to predict thermal demands using skin temperatures: Steady-state conditions

Machine learning approaches to predict thermal demands using skin temperatures: Steady-state conditions

Infrared thermography of human face for monitoring thermoregulation performance and estimating personal thermal comfort

Perbandingan Metode Klasifikasi untuk Menentukan Tingkat Kenyamanan Suhu pada Kondisi Rileks Berbasis Sinyal EEG

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