Enabling Novel Sensing and Interaction with Everyday Objects using Commercial RFID Systems

Abstract

The Internet of Things (IoT) promises an interconnected network of smart devices that will revolutionize the way people interact with their surrounding environments. This distributed network of physical devices will open up tremendous opportunities for Human-Computer Interaction and Ubiquitous Computing, creating novel user-centered and context-aware sensing applications. The advancement of IoT has been heavily focused on creating new and smart electronic devices, while the vast majority of everyday non-smart objects are left unchecked. Techniques based on active sensors are limited by their high deployment cost and the requirement for battery replacement. There currently exists a huge gap between the collection of devices integrated to the IoT and the remaining massive number of everyday physical objects. Radio-frequency identification (RFID) has been widely adopted in the IoT industry as a standard inventory management infrastructure. In this thesis, I apply signal processing and machine learning techniques on low-level channel parameters of commercially available RFID tags to enable novel sensing and interaction with everyday objects. These new sensing capabilities allow for our system to understand daily activities, create tangible user interfaces, and enhance user experiences related to human-robot interactions and object interactions in augmented reality.