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. INTRODUCTIONWithin the human factors and ergonomics community, there is an increasing emphasis on developing technologies and environments that not only accommodate a majority of users on a population scale, but also can be tailored to the singular demands of individuals engaging in specific activities. A key challenge to the implementation of so-called "contextaware" applications is the lack of access to reliable data that characterizes the user's behavioral context, as well as uncertainty about the optimal ways in which context-aware applications might take advantage of this information.We present a system that addresses these barriers in the implementation of an automated lighting control application. We will demonstrate the use of closed-loop feedback to enhance the performance of a sensor-enabled activity recognition system that drives an automated lighting system in an officestyle workspace. As a user engages in typical workplace activities, he or she activates sensors installed on objects in the environment.User activities are demonstrated implicitly as the individual interacts with objects or performs behaviors that are recognized by a computer vision system. Inferences about user behavior are generated by a multi-classifier engine running on a computer server embedded in a piece of office furniture. Explicit feedback about the user's reaction to the system's performance is elicited by a handheld computing application (i.e., a mobile phone "app") designed to prompt the user at moments when the system has detected a change in behavior.Responses to targeted questions are used in a reinforcement-learning paradigm to increase or decrease the likelihood that the system will consider the activation patterns to be representative of a specific behavior in the future. When thresholds of sufficient confidence are reached, the tunable LED...
Digitally controlled solid state lighting systems can afford a range of different qualities of light, adjustable to users' requirements. Sensor networks allow lighting changes to be actuated in response to the location, activities, and paths of the occupants. This paper reports initial results of an ongoing research to explore strategic control of a tunable LED system, in response to a preliminary activity recognition platform, as well as the associated human factors. Tunable LED panels connected to a sensor network were installed to illuminate three distinct occupied spaces: a private office, a public office space and a corridor at MIT Media Lab. Human factors experiments were conducted to assess visual acceptability under changing lighting conditions. In the first phase variations in color rendering were applied to verify perception of subtle changes in white lighting. Results from this phase indicate that it is possible to correlate activities with sensitivity to spectral change. In the second phase the question is how colored light can be used for energy savings and as a communication medium in these commercial spaces.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2025 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.