# Vision Based Dynamic Thermal Comfort Control Using Fuzzy Logic and Deep Learning
## 1 Introduction  
A large number of intelligent buildings have recently been developed with automated control systems [1; 2]. Different independent automation systems can be integrated in such buildings for resource sharing and efficient services [2]. The control of the thermal comfort in indoor environments is an important field in intelligent buildings. Different environmental parameters can be considered, such as occupancy, temperature, carbon dioxide levels, and relative humidity. These can be used to help control the thermal comfort level inside buildings. Some of the research studies have incorporated occupant preferences into thermal control systems. This can be employed either using a fixed set point regarding every single zone in the building or through occupants' feedback [3; 4; 5]. In other cases, occupants' behaviour information has been used to predict thermal comfort levels [6; 7]. However, this only happens over long periods, while an effective system must adapt to both long and short-term changes. An adaptive thermal heat balance model (ATHB) was proposed by [8]. This model integrated the adaptive approach with Predicted Mean Vote (PMV). The ATHB computes the predicted sensation votes when considering the variations of indoor environmental parameters and the running mean outdoor temperature. The metabolic rate was directly extracted from a pre-made table. The metabolic rate and clothes insulation were modified in relation to the outdoor temperature. The evaluation showed that the ATHB model performed well for naturally ventilated and air conditioned buildings.  
In fact, an occupant's preferences could also change, depending on their different daily activities due to the change of it's metabolic rate. Therefore, the adaptation tohuman actions is considered in this work. Importantly, the computer vision-based action recognition is used to associate an occupant's action to it's metabolic rate and then compute it's thermal comfort level. This gives the opportunity to avoid the need of using a sensor that is attached to an occupant all the time, such as in [9].  
Different variables should be considered in the computation of thermal comfort levels. These include environmental and subjective variables, such as temperature, humidity, clothes, and actions. The PMV model is one of the most important thermal comfort models [10]. The metabolic rate of the occupant is considered to be a main factor in the PMV calculation. Many research studies, such as [11; 12; 13], assume fixed metabolic rates, often set to 1 MET (for sitting, 4184 J/kg/h). However, the resulting PMV calculation is likely to produce an inaccurate estimate of the thermal comfort level due to the change of metabolic rate in the change of occupant activities.  
This work provides a novel adaptive methodology for thermal control that is based on occupant's actions using computer vision for action recognition in conjunction with a thermal comfort fuzzy control system. Deep learning and fuzzy logic techniques are selected in this work because they can simultaneously provide great performance and explainability for the intended application. Furthermore, these popular techniques is an area of on-going research, as can be seen in a number of modeling cases e.g., [14; 15; 16; 17].  
An occupant's actions are automatically associated with a metabolic rate. This is used in the PMV model calculation to predict the occupant thermal comfort levels. Finally, an adaptive context-aware fuzzy temperature control system is proposed to adjust thermal settings according to an occupant's actions. Our goal is to improve the traditional thermal model by presenting an adaptive method that takes detailed activity information into account. Figure 1 illustrates an overview of the proposed system.