In this demonstration, we present a closed-loop feedback system for evaluating and improving the human factors performance of a lighting system based on tunable LED technology. We investigate the ways in which closed-loop feedback can enhance the ability for lighting to automatically respond to changes in the user’s ongoing activities. A sensing platform uses multimodal wireless sensors and computer vision to detect an individual’s presence and uses computational reasoning to make inferences about his or her activities. A “recognition engine” provides access to the inferred activities, which the LED system uses to make contextually relevant lighting changes according to the various operational states within the space. A human factors experiment makes use of a mobile phone based context-aware experience sampling application that responds to changes in the activities, delivering questions to the user to help improve the activity classifier and refine the lighting application. During the demonstration, participants will experience lighting changes automatically applied to a workspace in order to fulfill the visual requirements of the detected activities and to maximize energy savings.
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