Investigation of the relationships between thermal sensations of local body areas and the whole body in an indoor built environment

Choi, Joon Ho; Yeom, Dongwoo

doi:10.1016/j.enbuild.2017.05.062

Cited by 40 publications

(23 citation statements)

References 32 publications

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“…Regarding the impact on OTC, only head and leg demonstrate significant influences. These results are in line with the findings of [232], in which the forehead and arm are indicated as appropriate local spots for OTS estimation. In [30] the main effect on OTS comes from the upper body.…”

Section: Models To Predict the Local And Overall Thermal Sensationsupporting

confidence: 91%

Models and Indicators to Assess Thermal Sensation Under Steady-State and Transient Conditions

Enescu

2019

Energies

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The assessment of thermal sensation is the first stage of many studies aimed at addressing thermal comfort and at establishing the related criteria used in indoor and outdoor environments. The study of thermal sensation requires suitable modelling of the human body, taking into account the factors that affect the physiological and psychological reactions that occur under different environmental conditions. These aspects are becoming more and more relevant in the present context in which thermal sensation and thermal comfort are represented as objectives or constraints in a wider range of problems referring to the living environment. This paper first considers the models of the human body used in steady-state and transient conditions. Starting from the conceptual formulations of the heat balance equations, this paper follows the evolution occurred during the years to refine the models. This evolution is also marked by the availability of increasingly higher computational capability that enabled the researchers developing transient models with a growing level of detail and accuracy, and by the validation of the models through experimental studies that exploit advanced technologies. The paper then provides an overview of the indicators used to characterise the local and overall thermal sensation, indicating the relations with local and overall thermal comfort.

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Section: Models To Predict the Local And Overall Thermal Sensationsupporting

confidence: 91%

Models and Indicators to Assess Thermal Sensation Under Steady-State and Transient Conditions

Enescu

2019

Energies

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“…Each algorithm can be applied to train a personal thermal comfort model based on the data-driven method, leading to 196 personal models in total. Some algorithms have been successfully applied previously to infer thermal comfort using environmental and/or physiological data, such as Classification and Regression Trees [19], Bayesian network [20,58], Logistic regression [23,26], J48 decision tree [59,60], and Random forest [26,38,61], and SVM [29]. In addition, the missing data in the total dataset were imputed using the K-nearest neighbors ("knn") algorithm.…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

Personal thermal comfort models with wearable sensors

Liu

Schiavon

Das

et al. 2019

Building and Environment

203

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A personal comfort model is an approach to thermal comfort modeling, for thermal environmental design and control, that predicts an individual's thermal comfort response, instead of the average response of a large population. We developed personal thermal comfort models using lab grade wearable in normal daily activities. We collected physiological signals (e.g., skin temperature, heart rate) of 14 subjects (6 female and 8 male adults) and environmental parameters (e.g., air temperature, relative humidity) for 2-4 weeks (at least 20 hours per day). Then we trained 14 models for each subject with different machine-learning algorithms to predict their thermal preference. The results show that the median prediction power could be up to 24% /78% /0.79 (Cohen's kappa/accuracy/AUC) with all features considered. The median prediction power reaches 21% /71% /0.7 after 200 subjective votes. We explored the importance of different features on the prediction performance by considering all subjects in one dataset. When all features included for the entire dataset, personal comfort models can generate the highest performance of 35% /76% /0.80 by the most predictive algorithm. Personal comfort models display the highest prediction power when occupants' thermal sensations is outside thermal neutrality. Skin temperature measured at the ankle is more predictive than measured at the wrist. We suggest that Cohen's kappa or AUC should be employed to assess the performance of personal thermal comfort models for imbalanced datasets due to the capacity to exclude random success.

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“…Therefore, it is important to keep a sufficient time length per designed thermal condition. Choi and Yeom [37,38] and Lee et al [39] recently determined 10-20 min as the time required to adapt subjects to thermal conditions in thermal comfort experiments. In addition, to measure the stable skin temperature of a person at rest using thermographic images, 10 min of acclimatization is needed [40].…”

Section: Methodsmentioning

confidence: 99%

Assessment of a Real-Time Prediction Method for High Clothing Thermal Insulation Using a Thermoregulation Model and an Infrared Camera

Lee¹,

Choi

Kim

et al. 2020

Atmosphere

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For evaluating the thermal comfort of occupants, human factors such as clothing thermal insulation (clo level) and metabolic rate (Met) are one of the important parameters as well as environmental factors such as air temperature (Ta) and humidity. In general, a fixed clo level is commonly used for controlling heating, ventilation, and air conditioning using the thermal comfort index. However, a fixed clo level can lead to errors for estimating the thermal comfort of occupants, because clo levels of occupants can vary with time and by season. The present study assesses a method for predicting the clo level of occupants using a thermoregulation model and an infrared (IR) camera. The Tanabe model and the Fanger model were used as the thermoregulation models, and the predicted performance for high clo level (winter clothing) was compared. The skin and clothing temperatures of eight subjects using a non-contact IR camera were measured in a climate chamber. In addition, the measured values were used for the thermoregulation models to predict the clo levels. As a result, the Tanabe model showed a better performance than the Fanger model for predicting clo levels. In addition, all models tended to predict a clo level higher than the traditional method.

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Investigation of the relationships between thermal sensations of local body areas and the whole body in an indoor built environment

Cited by 40 publications

References 32 publications

Models and Indicators to Assess Thermal Sensation Under Steady-State and Transient Conditions

Models and Indicators to Assess Thermal Sensation Under Steady-State and Transient Conditions

Personal thermal comfort models with wearable sensors

Assessment of a Real-Time Prediction Method for High Clothing Thermal Insulation Using a Thermoregulation Model and an Infrared Camera

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