Title: Enabling Novel Sensing and Interaction with Everyday Objects using Commercial RFID Systems
Advisor: Shwetak Patel
Supervisory Committee: Shwetak Patel (Chair), Joan Sanders (GSR, Bioengineering), Josh Smith, and Alanson Sample (Disney Research)
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. There currently exists a huge gap between the collection of devices integrated to the IoT and the remaining massive number of everyday objects that people interact with in their daily living.
Radio-frequency Identification (RFID) has been widely adopted in the IoT industry as a standardized infrastructure. In this thesis proposal, I apply signal processing and machine learning techniques to 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 recognize fine-grain daily activities, create tangible passive input devices, and enhance user experiences in human-robot interactions.