Cloud-based lighting control systems: Fatigue analysis and recommended luminous environments. (15th April 2022) - Choi, Seri; Choi, Anseop; Sung, Minki
Abstract: The development of Internet of Things (IoT) and the spread of various smart devices has made it easy to collect customized occupant information, and such smart devices and information have been applied to smart lighting control systems. Information on the personal characteristics of occupants can be utilized to create personalized luminous environment. This study focused on fatigue accompanying the activities of individuals and developed personalized smart lighting solutions to assist occupants in recovering their psycho-physiological state once they return home after performing daily activities. To this end, occupant activity information was analyzed in real time using an easily accessible mobile application and transmitted to a cloud-based platform, built using IBM Node-RED. Further, all daily activities and the number of steps taken from mobile application before returning home were used to quantify the fatigue level using the concept of Metabolic Equivalent Tasks (METs). Multiple regression analysis was employed to verify the accuracy between quantified fatigue level and subjective evaluation and to suggest resultant fatigue-level calculation formulas. From the calculated fatigue levels, the proposed luminous environment solution, such as appropriate illuminance and Correlated Color Temperature (CCT) values, was developed based on the results of previous relevant studies. Different luminous environments were implemented in a mock-up space as three case studies of different activity and fatigue levels. The proposed solutions are relevant to future intelligent and personalized building technologies to adapt from building-centric to human-centric environments by providing customized and automated indoor luminous environments. Highlights: A lighting control platform based on activity information analysis for fatigue relief was proposed. To collect personal activity data, a mobile application with built-in auto-sensing sensors was used. The fatigue significance level was tested using subjective evaluation and multiple regression analysis. Generally, high illuminance and low color temperature was provided to relieve the fatigue level.