## 5 Challenging Methods to Include Occupants Visual Comfort Perception Variance  
Various comfort indicators have been proposed and established. Nevertheless, the main problem remains how to address the continuous and large variance of occupants' comfort preferences, without largely deviating by applying robust approximations.  
[PERSON] et al. [79] highlights that \"_DGP_ models have some limitations for predicting glare in sunny climates with high luminance contrasts\", arguing that occupants could have higher glare tolerance, increasing the visual comfort variance. In fact, [PERSON] and [PERSON] [14] found from a survey analysis that visual comfort perception could be modified by age and type of job or activity executed. According to [PERSON] et al. [34] findings, older people's pupil behaviour tends to dilate slower than in the case of younger people but constricts faster. In addition, [PERSON] and [PERSON] [80] presented the relevant effect of ageing on the anatomical structure of the human eye, in particular for the cornea, the lens' thickness and lens' anterior radius; thus, modifying the eye's light regulation capacity.  
Finally, [PERSON] et al. [36] highlighted the degree of certainty of the effect of some of these factors on the occupants' visual perception:  
* gender and optical correction influence to be most certainly null;
* age, self-glare assessment, iris pigmentation impact to be yet inconclusive;
* culture, somewhat likely;
* macular pigment optical density (age-dependent), cortical hyperexcitability and contrast sensitivity influence have been considered likely.  
Consequently, to correct this variability based on their physical features the scientific community has proposed to monitor the natural body light perception regulatory system (i.e., the eye). [PERSON] [81] highlighted the human eye as the perfect light sensor and system actuator. As a personalized, naturally and highly evolved system, monitoring it could further calibrate visual comfort assessments (analogue to the body temperature in case of thermal comfort). For instance, the following approaches could be used:  
* analysing the way the pupil size varies with respect to a certain lighting provision [82; 83; 84; 85];
* monitoring pupil response when exposed to different lighting and work task type [86];
* screening the degree of eye opening [79];
* monitoring the view direction distribution and history under certain lighting provision [87];
* combining blinking, gaze direction, and pupil size variations records under a certain lighting environment [88]; and,
* analysing the frequency and extent of the facial muscles movement when exposed to expected discomfort glare [89].  
The above-stated facts motivated a new approach towards visual comfort level evaluation, by coupling the building occupants' involuntary (i.e., body) response to the building management systems (BMS). The actual paradigm in which BMS systems are operated is contrasted on the basis of the averaged surveyed indoor physical parameters (see Figure 3). Therefore, hoping to reduce the gap between modelled and operating buildings' performance, by linking the occupant visual response to the BMS and using the data for more comprehensive understanding of the actual visual comfort. Consequently, increasing the satisfaction of occupants with the indoor luminous environment while also improving building energy efficiency, potentially reducing the risk of Sick Building Syndrome occurrence [90] and enhancing the livability of indoor spaces.