Funded by the German Research Foundation (DFG) – 445336968
The current research project deals with developing an AI-based approach to the determination of dynamically changing, user-oriented luminaire control curves based on continuously recorded sensor input values to realize HCL-compliant system solutions for LED interior lighting. The term “control curve” can be used to summarize all those parameters that can be varied for each luminaire largely independently of one another and with time. For the planned investigations, these are the horizontal illuminance Eh generated on the work surface, the proportion of indirect light in this illuminance (according to Houser), the most similar color temperature (CCT), and the indirect blue light proportion, which significantly defines the physiologically activating component of the lighting and is to be described in the present research project with the help of the circadian stimulus (CS) value (according to Rea and Figueiro).
To determine optimal, AI-based control curves, test subject tests are carried out at different times of the day to determine the users' respective individual light preferences depending on both subjective-psychological and external influencing factors and to correlate them with the corresponding sensor data with the help of machine learning methods. The aim is to develop a lighting system that, based on the incoming sensor data and using an underlying user preference model, can predict the optimal control curve for the current situation and dynamically adjust the room's lighting accordingly over the course of the day.
As a starting point for the implementation of the planned research project, support points of individual control curves are initially recorded by recording user preferences related to the current light situation in the room at different times over several working weeks as part of a first test person study and using AI-supported evaluation with the simultaneously recorded sensor data Relation to be set. Based on this data, the support points determined in this way are summarized in individual, characteristic clusters depending on the ambient conditions as well as the subject-specific influencing factors (e.g., age, gender, chronotype, current state of mind, etc.), which correspond to similar lighting preferences of the test persons. In a second iteration step, these clustered support points are then presented to the participating test subjects in a further test subject study and further adapted to the individual preference using various setting options made available to the test subjects. Together with the continuously recorded sensor data, these additional input values further optimize the control curves. In the third part, a physiological evaluation of these optimal control curves in terms of maximizing user preference is to be carried out in the context of a further study by test persons to answer the question of their activating effect and finally to derive a corresponding user preference model as the basis for future, intelligent lighting systems.