A Multidimensional Continuous Contextual Lighting Control System Using Google Glass
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multidimensional-continuous-contextual.pdf
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7.36 MB
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Adobe PDF
Checksum (MD5)
0b322dd46bfbec38477365dd7225cd49
Author(s)
Zhao, Nan
Aldrich, Matthew Henry
Reinhart, Christoph
Paradiso, Joseph A.
Date Issued
November 2015
Journal
Proceedings of the 2nd ACM International Conference on Embedded Systems for Energy-Efficient Built Environments - BuildSys '15
Publisher
Association for Computing Machinery (ACM)
Citation
Zhao, Nan, Matthew Aldrich, Christoph F. Reinhart, and Joseph A. Paradiso. “A Multidimensional Continuous Contextual Lighting Control System Using Google Glass.” Proceedings of the 2nd ACM International Conference on Embedded Systems for Energy-Efficient Built Environments - BuildSys ’15 (2015), November 4-5, 2015, Seoul, South Korea.
Version
Author's final manuscript
Abstract
An increasing number of internet-connected LED lighting fixtures and bulbs have recently become available. This development, in combination with emerging hardware and software solutions for activity recognition, establish an infrastructure for context-aware lighting. Automated lighting control could potentially provide a better user experience, increased comfort, higher productivity, and energy savings compared to static uniform illumination. The first question that comes to mind when thinking about context-aware lighting is how to determine the relevant activities and contexts. Do we need different lighting for reading a magazine and reading a book, or maybe just different lighting for reading versus talking on the phone? How do we identify the relevant situations, and what are the preferred lighting settings? In this paper we present three steps we took to answer these questions and demonstrate them via an adaptive five-channel solid-state lighting system with continuous contextual control. We implemented a multidimensional user interface for manual control as well as an autonomous solution using wearable sensors. We enable a simple set of sensors to manipulate complicated lighting scenarios by indirectly simplifying and reducing the complexity of the sensor-lighting control space using human-derived criteria. In a preliminary user study, we estimated significant energy savings of up to 52% and showed multiple future research directions, including behavioral feedback.
MIT Department
Massachusetts Institute of Technology. Department of Architecture
Massachusetts Institute of Technology. Media Laboratory
Massachusetts Institute of Technology. Sustainable Design Lab
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/2821650.2821673
