## 3 Results
### Thermal Comfort Models
Examples of simplified PMV models are reported in Table 4. In almost all cases, linearization or linear regression techniques allowed us to simplify PMV model to incorporate it in a complex calculation framework. Input quantities are usually a reduced set of the original model's parameters, but occasionally the simplified function relates PMV with technological variables. It can be observed that personal factors are never part of the simplified models' input set, but they were assumed as constant during the model construction process. Figure 8: Reviewed documents classified by thermal comfort models: overall (**left**) and breakdown of “non-PMV” sector (**right**). \begin{table}
\begin{tabular}{l l} \hline \hline
**Reference** & **Thermal Comfort Models** \\ \hline [PERSON] et al. (2017) [30] & • PMV \\  & • Adaptive \\ \hline [PERSON] and [PERSON] (2018) [31] & • PMV \\  & • Adaptive \\ \hline [PERSON] et al. (2019) [32] & • PMV, extended PMV, adaptive PMV \\  & • Predicted thermal state \\  & • Gender-based (male/female) thermal state \\  & • Temporal profile thermal state \\ \hline [PERSON] et al. (2019) [33] & • PMV \\  & • Adaptive \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Studies using multiple thermal comfort models. Over half of the PMV-based studies made simplifications on the input parameters, especially clothing insulation and metabolic rate, by assuming them on the basis of standards or public databases (see, for example, the \"Compendium of Physical Activities\" [48]). Relatively few attempts to estimate these parameters more accurately and in real time can be found among the reviewed papers. [PERSON] et al. [49] obtained \(M\) from wearable devices as a function of heartbeat, breathing rate, posture, activity level, and acceleration module. [PERSON] and [PERSON] [50] calculated metabolic heat gain of human body from occupant thermal model. [PERSON] et al. [51] evaluated \(M\) as a 10 min moving average of metabolic equivalent of task based on walking speed, which is a function of height and body mass. [PERSON] et al. [52] proposed a clothing insulation system recognition based on real-time frames from a camera, relying on a convolutional neural network model built from a large garment image database. [PERSON] et al. [53] used machine learning algorithms to obtain \(M\) and \(I_{\text{cl}}\) from camera images within a discrete range of possible values. Another frequent assumption is the equivalence of mean radiant temperature with air temperature, which is usually motivated by either sensitivity analyses, as in [54], or simplicity reasons. The estimation of MRT is indeed a complex task, as it can be performed via expensive instrumentation (black globe thermometer) or with one of several calculation procedures exploiting the readings from an adequate number of surface temperature sensors. However, MRT is a key parameter in thermal comfort perception, and simply assuming it equal to air temperature without further validation can compromise the model-predictive capabilities, particularly in old buildings with high window-to-wall ratios [9]. Most of the PMV-based works analyzed in this review made the temperature equivalence assumption without providing a reason. The few that estimated MRT obtained it from