ChopTags: An Accurate and Low-cost Interface to Identify User/Item Interactions

Identifying item-item and user-item interactions is an essential requirement of many ubiquitous computing applications. Recently methods of physically altering RFID tag hardware have been proposed to enable recognizing certain interactions. However, they do not address the problem that when a large number of tags exist in the environment and concurrent interactions may happen, the system may not be able to identify these interactions accurately or efficiently. We propose ChopTags, a low-cost and accurate interaction identification using passive RFID tags, with applications including automatic chess notation, shipment storage tracking, interactive libraries/retail stores/classrooms, and smart conference badges to track the attendees who had conversations. Each ChopTags module contains a passive tag chip and an antenna that are separated and can only be read when the chip is in contact with an antenna (from another pairing ChopTags module). ChopTags costs cheap hardware to scale to many users and items, achieves near 100% accuracy in complex environments, and is easy to use for children, seniors, and others who have difficulty of operating smart devices. We resolve a number of challenges of using ChopTags including improving query throughput/accuracy and identifying concurrent interactions. We build two prototypes based on ChopTags: 1) a chess auto-notation system and 2) a tag array for user interactions. ChopTags allows tracking the moves of 96 tag modules for the chess game with almost 100% accuracy and no prior work can achieve this.