## 5 Challenging Methods to Include Occupants Visual Comfort Perception Variance
### Monitoring Occupants' Field of View  
Recalling the information gathered in Tables 2-4 and the comparison made in Figure 4, personalized and, if possible, image-based analysis seems to be the most comprehensive and convenient way of assessing visual comfort. In this way, task lighting sufficiency,  
Figure 4: Direct comparison of the most utilized metrics under 5 different criteria, providing a subjective and experience-based rating, and highlighting in yellow the highest ranked in: (**a**) completeness of relevant factors considered (**b**) degree of occupant-centred approach (**c**) ease of modelling in the design phase (**d**) ease of monitoring in the operation phase (**e**) ease of computing. contrast and glare risk can be evaluated simultaneously. For this purpose, different methods have been proposed and hereby summarized; however, difficulties arise considering the limitations of replicating simulation-based analysis within real scenarios. * Image-based--Although these methods demand higher computation time, they are the ones capable of capturing different aspects that, as discussed previously, can alter the perception of the surrounding luminous environment. * [PERSON] et al. [112] proposed an innovative image-based methodology on the evaluation of contrast able to take into consideration visual impairments in health. The proposed method screens the produced images, identifying disturbing surfaces or elements within the field of view, based on a set threshold of contrast of luminance between two juxtaposed surfaces. * Based on the work of [PERSON] et al. [113] for the automotive sector, aiming to reduce night accidents due to wrong street lighting disposition and intensity; [PERSON] et al. [114] studied the possibility of automatizing this methodology for estimating glare risk, \(L\) and \(E_{v}\) in real-time by employing Charge Coupled Device (CCD) cameras mounted on a vehicle. * Based on the results obtained by [PERSON] and [PERSON] [63], [PERSON] [115] presented an actual office space survey on 14 participants, coupling HDR images analysis (captured with CCD cameras oriented to the most frequent occupants' field of view) with polling station results on visual comfort rating; in order to test the accuracy in terms of glare rating estimation, with horizontal and \(E_{v}\) based computations. * Using CCD cameras as a data acquisition system, it is possible to make, calibrate, and compute \(E_{v}\) and \(L\) from HDR images. These can be later used to monitor the interior conditions of an environment, as done by [PERSON] et al. [116] and [PERSON] [117]. The latter proposed the calculation by using the RGB values of an image and the \(D_{65}\) reference illuminant, which after the proper calibrations, and accounting for the camera settings (e.g., exposure, focal length), obtained errors below 8% when testing the methodology under different light sources. On the other hand, [PERSON] [118] proposed a geometrical-based procedure to understand the illuminance value coming from a certain surface using CCD cameras and HDR images, acknowledging not only the camera settings, but also the distance between the objects and the camera. Under controlled conditions, the error obtained was below 5%. * [PERSON] et al. [119] tested the computation of \(DGP\) from HDR images created from low-resolution camera pictures while monitoring \(E_{h}\), to establish a venetian and roller blind control algorithm aiming to avoid the occurrence of visual discomfort. The \(DGP\) was initially calculated, and then the strategy was tested on those users which interacted with the shading systems to increase daylight influx indoors. However, the \(E_{h}\) was found to be underestimating the light intensity adaptation, and the \(DGP\) to be overestimating the glare risk rating. * [PERSON] et al. [120] used two HDR ceiling vision sensors for monitoring work-plane illuminance (ceiling mounted) and \(DGP\) (oriented towards occupants' visual display terminal (VDT)).