Personalized optimal room temperature and illuminance for maximizing occupant's mental task performance using physiological data

Indoor room temperature and illuminance level are critical factors of indoor environment quality (IEQ), affecting human mental task performance. These effects are reflected in their physiological responses such as heart rate, electrodermal activity, and skin temperature. Occupants' individual preferences, sensitivity, and physiological responses to different combinations of room temperature and illuminance level can differ among individuals. Despite previous studies investigating the individual and combined effects of different IEQ parameters, the limited research on the crossmodal relationship between room temperature and illuminance level and its impact on mental task performance highlights its significance. Moreover, to achieve personalized insights, it is essential to incorporate individual physiological responses, and this necessitates the development of an optimization model to comprehensively examine their impact. To address these issues, this study proposes a personalized model that optimizes room temperature and illuminance levels to enhance mental task performance using occupants' physiological data. Having the random forest algorithm, this study first predicted mental task performance, which includes four mental abilities such as attention, perception, working memory, and thinking ability using the occupant's physiological data. Then, the particle swarm optimization algorithm was employed to optimize room temperature and illuminance level to maximize the predicted mental task performance. The results of the proposed model align with observed values of room temperature and illuminance level during experiments, validating the adoption of a personalized approach. The findings contribute to future insights and guidelines for the design and management of indoor environments to maximize occupants' performance.