Modeling of Personal/Group Dynamic Comfort Space Based on Asymmetric Gaussian Function

A dynamic comfort space model based on asymmetric Gaussian function is designed. Firstly, the shapes of comfort spaces at different speeds are determined according to the motion state information of a human. Then a scalable fuzzy reasoning framework is proposed to define the size of personalized comfort space by considering the differences of individual social attributes such as gender and age. Based on the comfort space model of an individual person, the concept of common concern area is proposed, and the case of human group is analyzed through the minimum covering circle algorithm, so as to construct the comfort space for the group. Qualitative as well as quantitative experiments are carried out to analyze and evaluate the effectiveness of the proposed dynamic comfort space model, and the rationality of the model is verified.