## 5 Challenging Methods to Include Occupants Visual Comfort Perception Variance
### Monitoring Occupants' Work Plane  
When monitoring the occupants' work plane, the collected strategies were found to be mainly focused on surveying \(E_{h}\) intensities and contrast (\(U_{o}\)). These strategies are easily applied on simulation-based design, likewise replicated in real-case scenarios. For instance:  
* [PERSON] et al. [100] presented a platform-based design framework for BMS considering data acquired from a camera, illuminance, temperature, CO\({}_{2}\), relative humidity (RH) and passive infra-red (PIR) sensors, and occupants' requests via mobile applications; managing decisions throughout the use of fuzzy logic to run HVAC and lighting appliances.
* [PERSON] et al. [101] proposed the automation of shared lighting appliances through game theory for processing the building occupants' vote on lighting preferences.
* [PERSON] [102] presented an approach of a light-switching and blind-operation model within a two occupants office, in which using simulation methods approach resulted in a 20% lighting energy reduction achieved by monitoring at every desk: occupants presence, light and radiation intensity, light appliances condition (ON/OFF) and predicting the possibility of turning them ON upon arrival [103], or during the day [22], and of turning them OFF [104].
* [PERSON] et al. [105] tested and studied the work-plane illuminance set-points of a control strategy for light-switching and blind operation on single office space through simulations with EnergyPlus with dynamically updated operation thresholds (modified by the probability models of user-interaction [103; 104; 22]). As a result, energy use reductions between 25-35% were achieved.
* [PERSON] et al. [106] studied the personal occupant visual comfort in parallel with energy savings (weighting cooling vs. lighting energy needs) and slat inclination angles of venetian blinds, monitoring lighting conditions with illuminance sensors on the desk and occupants interactions within a recreated office space facing east. The acquired datas was used to train and adjust the thresholds through a Q-learning process, leading to an energy saving potential up to 10%.
* [PERSON] et al. [107] proposed a different approach by studying ceiling-mounted sensor configuration through simulations in DIALux [108]. Using multiple intelligent dimmable luminaries equipped with occupancy and light sensors. These were calibrated at night to reach \(E_{h}=500\) lux, to maintain desired lighting conditions working as stand-alone systems or as an integrated system network (for optimized luminaries activation also see [PERSON] et al., [PERSON] and [PERSON] [109; 110]).
* [PERSON] et al. [111] studied the Indoor Environmental Quality (IEQ) by continuously monitoring an office area by a moving punctual reading (mounting sensors on a robot, including \(E_{h}\)). Doing so, it is possible to interpolate and map the total area conditionswith decent accuracy, given the surveyed values obtained while moving the robot throughout a predefined path.  
Nevertheless, comparing the previously mentioned methods and more frequently utilized metrics, most of them analysed the light intensity on the work-plane, neglecting the light intensity and contrast perceived at eye level. Doing this is rather challenging; nonetheless, other methodologies have been elaborated based on the field of view to assess visual perception with higher accuracy, giving more importance to the light intensity falling on the human eye, its distribution around the space, and the personal occupant preferences (see Figure 4).