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Title: Towards Ubiquitous Self-Powered Ambient Light Sensing Surfaces
Dingtian Zhang
Ph.D. Student in Computer Science
School of Interactive Computing
College of Computing
Georgia Institute of Technology
Date: Friday, January 10th, 2020
Time: 10:00am - 12:00pm (EST)
Location: CODA 1215
Committee
Dr. Gregory D. Abowd (Advisor), School of Interactive Computing, Georgia Institute of Technology
Dr. Thad Starner, School of Interactive Computing, Georgia Institute of Technology
Dr. Sauvik Das, School of Interactive Computing, Georgia Institute of Technology
Dr. HyunJoo Oh, School of Industrial Design & School of Interactive Computing, Georgia Institute of Technology
Dr. Canek Fuentes-Hernadez, School of Electrical and Computer Engineering, Georgia Institute of Technology
Abstract
Human activities implicitly or explicitly block the path of ambient light in our environment, resulting in light changes on the surfaces of everyday objects. We can leverage these interference patterns of ambient light as a general-purpose, privacy-preserving signal to support activity recognition as well as novel interaction techniques. In this work, we will expand a design space for ambient light sensing to enable a wide range of applications from walking activity detection to multitouch inputs. With the development of OptoSense, we introduce a self-powered research platform for the quick prototyping of ambient light sensing surfaces using off-the-shelf silicon-based photodetectors and photovoltaics. OptoSense is cost-effective and can work with a variety of form factors that conform to everyday surfaces. We show a rich application space of OptoSense with three form factors (0D, 1D, 2D) and its robustness under different lighting conditions. With the use of organic semiconductor (OSC) devices that are inherently compatible with arbitrary shapes, thin and flexible form factors, and scalable manufacturing processes, we show a path towards ubiquitous ambient light sensing surfaces.
In the proposed work, we will introduce OptoSense 2.0 system using photodetectors with unconventional patterns. These sensors are designed to extract mid-level features directly from motion events such as human activities and interactions, and encode ambient light into application-specific signals. These “computational” sensors could reduce the complexity of the rest of the system and provide additional power, cost, and privacy benefits, enabling a greater number of applications such as inventory tracking and ad-hoc interfaces. The use of OSC devices brings further advantages in sensor fabrication with novel form factors.
