## 2 Related Work  
Since the 1970s, [PERSON] has explored human thermal comfort and conducted many kinds of subjective experiments. Based on this, he eventually established what is known as [PERSON]'s theory [27]. From then on, many studies about thermal comfort were carried out. Questionnaire surveys are good as a method to understand the inner feeling of an occupant. With the development of the internet, online questionnaire surveys can also be generated [3; 4]. However, it is inconvenient and also difficult to guarantee that occupants will continue to give feedback based on their personal thermal feelings [5]. Therefore, the environment measurement method was also adopted in the building industry [6]. With this kind of method, some objective parameters, such as indoor temperature, airflow and humidity, are often measured. Unfortunately, the goal of the environment measurement method is to meet the thermal comfort needs of a majority of indoor occupants. Therefore, the thermal feelings of a minority were ignored. To overcome this drawback,a kind of nonlinear autoregressive network, still belonging to the environment measurement method, was generated to predict indoor temperature [7]. In fact, human thermal comfort is complicated, and with constant indoor parameters it is difficult to meet each individual's requirements for thermal comfort. As such, some researchers study physiological measurement methods, including the contact measuring method, semi-contact measuring method and contactless measuring method. For the contact measuring method, skin temperature and heart rate are usually the measured parameters. [PERSON] and [PERSON] [8; 9] made early attempts at measuring the skin temperature around the human body. [PERSON] [10] conducted subjective experiments and a total of 22 subjects were invited. The data, being local skin temperatures and electrocardiograms, were collected. Based on these collected data, 26 measuring methods of mean skin temperature were assessed. [PERSON] [11] presented a multiple regression equation to predict skin temperature in non-steady state. The multiple regression equation was considered as a function of mean skin temperature. Wrist skin temperatures and upper extremity skin temperatures were also adopted to estimate human thermal sensation, respectively [12; 13]. [PERSON] [14] presented a predicted thermal state (PTS) model, and the capture of peripheral skin temperature. Furthermore, body surface area and clothing insulation were used for analyzing inter- and intra-individual differences. As for the thermal comfort study using heart rate, based on physiological experimentation, [PERSON] [15] investigated the relationship between heart rate variation (HRV) and electroencephalograph (EGG). The results show that HRV and EEG are useful for thermal comfort studies, but further data validation is needed. Moreover, [PERSON], [PERSON] and [PERSON] [16; 17; 18] combined machine learning with contact measurement, and they are all meaningful attempts. However, as the linear kernel of SVM was used for predicting human thermal sensation in different experiments, sometimes overfitting can happen. For the semi-contact measuring method, [PERSON] [19] used an infrared sensor to estimate skin temperature of different face points. The infrared sensor was mounted on the frame of eyeglasses. Based on this, a hidden Markov model was constructed to capture personal thermal sensation [20]. In practical application, contact and semi-contact measurement are both difficult to apply widely. The reason is that an occupant needs to wear a sensor, which is uncomfortable and is also not in line with the goal of human-oriented intelligent buildings. For this reason, a kind of contactless measuring method was studied in reference [21]. Based on vision sensors, [PERSON] [21] extracted the saturation (S) channel from skin images and constructed two saturation-temperature models to estimate skin temperature.