Ubiquitous Self-Powered Ambient Light Sensing Surfaces

Abstract

Many human activities interfere with ambient light in a predictable and detectable way in that our activities implicitly or explicitly block the paths of ambient light in our environment. This dissertation explores sensing of ambient light interference patterns as a general-purpose signal at the surface level of everyday objects for activity recognition as well as novel interaction techniques. Two sensing systems, OptoSense and the Computational Photodetector, are developed as self-powered, cost-effective, and privacy-preserving computational materials that sense ambient light interference patterns and detect a wide variety of implicit and explicit human activities on surfaces of everyday objects. This work shows a promising path of a ubiquitous computational material that weaves into the fabric of everyday surfaces, and discusses the challenges and opportunities from the power, cost, privacy, form factors, and application perspectives.