# Towards Online Personalized-Monitoring of Human Thermal Sensation Using Machine Learning Approach
## 1 Introduction  
Thermal comfort (TC) is an ergonomic aspect determining satisfaction with the surrounding environment and is defined as 'that condition of mind which expresses satisfaction with the thermal environment and is assessed by subjective evaluation' [1]. The effect of thermal environments on occupants might also be assessed in terms of thermal sensation (TS), which can be defined as 'a conscious feeling commonly graded into the categories cold, cool, slightly cool, neutral, slightly warm, warm, and hot' [1]. Thermal sensation and thermal comfort are both subjective judgements, however, thermal sensation is related to the perception of one's thermal state, and thermal comfort is related to the evaluation of this perception [2]. In other words, TS expresses the perception of the occupants, while TC assesses this perception, taking into account physiological and psychological factors [3]. The assessment of thermal sensation has been regarded as more reliable and as such is often used to estimate thermal comfort [4]. Thermal sensation is the result of the body \"psycho-physical reaction\" to certain thermal stimuli related to indoor conditions [5]. Human thermal sensation mainly depends on the human body temperature (core body temperature), which is a function of sets of comfort factors [5; 6]. These comfort factors include indoor environmental factors, such as mean air temperature around the body, relative air velocity around the body, humidity, and mean radiant temperature of the environment to the body [6]. Additionally, some personal (individual-related) factors, namely metabolic rate or internal heat production in the body, which vary with the activity level and clothing thermo-physical properties (such as clothing insulation and vapor clothing resistance), are included. It should be mentioned that the individual thermal perception is deepening, as well, on psychological factors, expectations and short/long-term experience, which directly affect individuals' perceptions, time of exposure, perceived control, and environmental stimulation [7]. The most considered way to have an accurate assessment of TS is to ask the individuals directly about their thermal sensation perception [5; 6]. The thermal-sensation-vote (TSV) is one of the most used concepts to address the opinion of individuals concerning TS. That is, individuals express their vote to rate their TS when they are exposed to given thermal conditions, by using a scale from cold to hot, with a predefined number of points. Thermal sensation mathematical models are developed in order to overcome the difficulties of direct enquiry of subjects. The development of such models is mostly dependent on statistical approaches by correlating experimental conditions (i.e., environmental and personal variables) data to thermal sensation votes obtained from human subjects [4; 6]. The recent intensive review work of [PERSON] (2019), explored the most important contributions to model and predict thermal sensation (TS) under both steady-state and transient conditions. It is shown that the most used models to assess TS of the human body with respect to the environment have been developed starting from [PERSON]'s predicted-mean-vote (PMV) empirical model [3] for steady-state conditions and from the Gagee model [8] for transient conditions. Since then, numurus models are developed to assess and predict TS (e.g., [9; 10; 11; 12; 13; 14; 15; 16]). Most of the aforementioned models (e.g., PMV) are static in the sense that they predict the average vote of a large group of people based on the seven-point thermal sensation scale, instead of individual thermal comfort, they only describe the overall thermal sensation of multiple occupants in a shared thermal environment. To overcome the disadvantages of static models, adaptive thermal comfort models aim to provide insights in increasing opportunities for personal and responsive control, thermal comfort enhancement, energy consumption reduction and climatically responsive and environmentally responsible building design [17; 18].