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Developing a Data-Driven Model of Overall Thermal Sensation based on the Use of Human Physiological

Traditional air-conditioning methods maintain temperatures in a whole room at a constant level, and much work has been done to assess and improve the thermal comfort and sensations of people in a building environment. This study endeavors to identify the potential of using some local body area for predicting thermal stress. A total of 20 human subjects were tested in the University of Southern California’s climate chamber to determine their physiological parameters and subjective perceptions of environment. Ambient temperature was documented during the tests, while the human subjects were exposed to a warm, cool, or neutral environment. Based on these tests, correlation analysis and algorithms are applied to identify the relative thermally sensitive skin areas, their contribution rate to the overall thermal sensation, and potential skin area combinations that have high correlation with thermal sensation. Besides, the study also identifies the different impacts of local thermal sensation and local skin temperature while predicting the overall thermal sensation. When only the baseline attributes (environment temperature, Body Mass Index [BMI], gender) are considered, the estimation can be 94.5%. If baseline attributes are combined with the temperature of one local spot on the skin, the estimation accuracy can be around 97%; on the other hand, if baseline attributes are combined with one local thermal sensation, the estimation accuracy can be around 98%.With the correlation analysis and the application of the data-driven model, a method that uses some local body area to predict the overall thermal sensation can be developed.this inconsistency could also be attributed to the human factor of each subject, such as gender and BMI.

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