GaussRFID: reinventing physical toys using magnetic RFID development kits

Rong-Hao Liang, Han-Chih Kuo, Bing-Yu Chen

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

5 Citations (Scopus)

Abstract

We present GaussRFID, a hybrid RFID and magnetic-field tag sensing system that supports interactivity when embedded in retrofitted or new physical objects. The system consists of two major components - GaussTag, a magnetic-RFID tag that is combined with a magnetic unit and an RFID tag, and GaussStage, which is a tag reader that is combined with an analog Hall-sensor grid and an RFID reader. A GaussStage recognizes the ID, 3D position, and partial 3D orientation of a GaussTag near the sensing platform, and provides simple interfaces for involving physical constraints, displays and actuators in tangible interaction designs. The results of a two-day toy-hacking workshop reveal that all six groups of 31 participants successfully modified physical toys to interact with computers using the GaussRFID system.
Original languageUndefined
Title of host publicationProceedings of the 2016 CHI Conference on Human Factors in Computing Systems
Place of PublicationNew York
PublisherAssociation for Computing Machinery, Inc
Pages4233-4237
Number of pages5
ISBN (Print)978-1-4503-3362-7
DOIs
Publication statusPublished - 7 May 2016
Externally publishedYes
Event34th Annual ACM CHI Conference on Human Factors in Computing Systems (CHI 2016) - San Jose, United States
Duration: 7 May 201612 May 2016
Conference number: 34
https://chi2016.acm.org/wp
https://chi2016.acm.org/wp

Conference

Conference34th Annual ACM CHI Conference on Human Factors in Computing Systems (CHI 2016)
Abbreviated titleCHI 2016
CountryUnited States
CitySan Jose
Period7/05/1612/05/16
Internet address

Cite this

Liang, R-H., Kuo, H-C., & Chen, B-Y. (2016). GaussRFID: reinventing physical toys using magnetic RFID development kits. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (pp. 4233-4237). New York: Association for Computing Machinery, Inc. https://doi.org/10.1145/2858036.2858527