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Human thermal comfort model is of great significance for improving the thermal comfort of the building’s environment indoors. Physiological parameters can reflect the change of human thermal comfort directly and effectively, which are crucial to establish thermal comfort model of human body. Skin electrical signal is one of the most important physiological parameters to reflect the human thermal response, so it’s completely useful for the model. Feature extraction directly affects the model’s accuracy when the feature vector is used as a part of the model input and the thermal comfort value of human body serves as the model’s output. Based on the wavelet packet transform, a new method about extracting features is studied for human thermal comfort model in this paper. The correctness of the method is proved to be effective by experiments; at meanwhile, the experimental results show that the skin electrical signal can reflect the thermal comfort of human body and its features can be used as a condition for the identification of human thermal comfort.

Skin Electrical Signal Feature Extraction Based on Wavelet Packet and Establishment of Human Thermal Comfort Model