## 5 Challenging Methods to Include Occupants Visual Comfort Perception Variance
### Eye Response to Light Variations
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Metric** & **Advantages** & **Disadvantages** \\ \hline \(d\) & Accounts for directionality and human adaptation, highly responsive and reliable, light at eye known. & There is no established correlation with the occurrence of glare, no human variability, only one location, hard to measure and model. \\ _NPS_ & Accounts for directionality and human adaptation, refers to the saturation level of adaptability. & There is no established correlation with the occurrence of glare nor illuminance, only one location, hard to measure (especially for neutral condition) and model. \\ _TEPR_ & Measures pupil kinematics with high precision. & Highly intrusive measurement, no correlation with lighting environment performed, only pupil response recorded. \\ \(D_{U}\) & Accounts for directionality, human adaptation and variability. Highly responsive and reliable. Light at eye known. & There is no established correlation with the occurrence of glare, only referred to one location and occupant, not applicable for young occupants, hard to monitor and model. \\ _DEO_ & Accounts for directionality and human adaptation, refers to saturation level of adaptability (not only pupil), correlation with _DGP_. & Scaling issues, only actions for eye-lid kinematics considered, no human variability considered, intrusive measurement, one location, hard to model. \\ _E\({}_{\text{sg}}\)_ & Accounts for directionality and human adaptation, indirectly refers to adaptability (not only pupil), correlation with _DGP_. & No human variability considered, intrusive measurement, one location, hard to model (requires known gaze behaviour). \\  & Light at eye known. & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Critical analysis summary of visual perception metrics based on eye conditions, highlighting their benefits and drawbacks. to tune automatic blind control to regulate light influx. [PERSON] et al. [97] further analysed such components, testing a predictive model to later anticipate the occupants' intentions to regulate the window-blind states. Likewise, [PERSON] [98] presented a general conceptual work-flow to deal concurrently with heating, cooling, ventilation, lighting and automated blinds operation; by allowing occupants to communicate their interaction desires and by collecting data inputs of the weather forecast. In more detail, recent research has concentrated on studying comfort from the singular occupant perspective. Occupant-based metrics have been proposed, as [PERSON] et al. [99] did by normalizing results with occupancy density. This approach shift provides the advantage of understanding the real impact and needs of occupants' micro-conditions by having sensors and actuators close to the building occupants' workstations (in case of office buildings) and remotely controlled actuators from mobile or website applications (in case of residential buildings). From the studied actuators, their operation was found to be more in line with occupants' preferences (i.e., accurate response), daylighting proxies (parameters) were monitored at the occupants' work plane or from the occupant's view perspective.