## 1 Introduction
[PERSON] [14] achieved thermal comfort prediction by collecting human skin temperature, which was substantially more accurate compared to the traditional \(PMV\) model. [PERSON] et al. [15] established a prediction model using a classification tree to realize the prediction of individual thermal comfort, further analyzed the influence of physiological parameters and thermal history on individual thermal comfort and verified the accuracy and reliability of the model through experiments. [PERSON] et al. [16] used environmental monitoring devices to obtain indoor environmental parameters and wearable devices such as wristbands to measure skin temperature, heart rate, and other physiological parameters to predict the user's thermal comfort. [PERSON] et al. [17] measured human skin temperature and heart rate with a wristband device and combined them with environmental parameters for thermal comfort prediction. With the development of computer vision technology, the non-contact physiological parameter detection method has been widely used. An innovative infrared imaging method has been put forward by [PERSON] et al. [18] to measure skin temperature and consequently analyze an individual's level of thermal comfort. [PERSON] et al. [19] proposed a novel non-invasive infrared thermography framework to collect skin temperatures from different parts of the body as a way to estimate the occupant's thermal comfort level. [PERSON] et al. [20] used non-contact sensors to detect heart rate to predict people's thermal comfort. [PERSON] et al. [21] combined sensors and thermal image recognition techniques to obtain human physiological parameters to predict human thermal comfort. However, thermal comfort is a subjective feeling of the human body and the method of physiological parameter detection ignores the influence of human psychological factors on thermal comfort generation. As a psychological representation of individual thermal comfort, thermal discomfort body language offers new research directions and opportunities for individualized thermal comfort measurement and modulation [22; 23]. Body language expressions related to thermal comfort were carefully observed by [PERSON] et al. [24], who found that thermal discomfort body language had a high degree of consistency in assessing individual thermal feelings. By using a Kinect camera to record postures associated with thermal discomfort, [PERSON] et al. [25] developed a \"thermal discomfort posture library\" that could be used to measure different thermal discomfort levels. [PERSON] et al. [26] confirmed the feasibility of using human posture to assess thermal comfort and proposed an algorithm for the identification of 12 postures related to thermal comfort to achieve real-time contactless thermal comfort assessment. Thermal comfort for occupants should ideally be maintained adaptively by adjusting the temperature in response to occupant actions. Many studies have demonstrated that _PMV_ can detect and maintain thermal comfort levels. Another advantage is that it can deliver better energy savings than traditional control systems [27; 28]. Although body language can express psychological differences in thermal comfort, given the uncertainty of body language, a separate analysis of the group's thermal comfort level will make it difficult to effectively take into account the commonality of thermal comfort characteristics. Therefore, it would be more effective to combine generally accepted indicators of group thermal comfort with individualized measures of thermal discomfort body language. To solve the above problems, this study proposed a prediction model of the thermal dissatisfaction rate based on the expression of thermal discomfort body language and further realized the online regulation of room temperature.