We present TRing, a finger-worn input device which provides instant and customizable interactions. TRing offers a novel method for making plain objects interactive using an embedded magnet and a finger-worn device. With a particle filter integrated magnetic sensing technique, we compute the fingertip’s position relative to the embedded magnet. We also offer a magnet placement algorithm that guides the magnet installation location based upon the user’s interface customization. By simply inserting or attaching a small magnet, we bring interactivity to both fabricated and existing objects. In our evaluations, TRing shows an average tracking error of 8.6 mm in 3D space and a 2D targeting error of 4.96 mm, which are sufficient for implementing average-sized conventional controls such as buttons and sliders. A user study validates the input performance with TRing on a targeting task (92% accuracy within 45 mm distance) and a cursor control task (91% accuracy for a 10 mm target). Furthermore, we show examples that highlight the interaction capability of our approach.
TRing: Instant and Customizable Interactions with Objects Using an Embedded Magnet and a Finger-Worn Device
Authors: Sang Ho Yoon, Yunbo Zhang, Ke Huo, Karthik Ramani
In Proceedings of the 29th Annual ACM Symposium on User Interface Software & Technology (UIST'16) , Tokyo, Japan, 2016 (Acceptance Rate: 20.6%)
Sang Ho Yoon
Sang Ho Yoon is currently working at Microsoft, Seattle, WA. He received his PhD at Purdue University and his B.S & M.S degrees from Carnegie Mellon University in 2008 with major in Mechanical Engineering and minor in Robotics. He worked at Research Department in LG Display & LG Electronics for 5 years. There, he involved in product development for consumer electronics as well as the futuristic products including 'Transparent & Public Display', 'Assistive/Rehabilitation Robot', and 'Smart Car User Interface'. He is particularly interested in applying novel sensing techniques to bring the new forms of input metaphor for Human-computer interaction. Areas of interest include wearable/tangible interface, sensing techniques & fabrication, and novel input device. Currently, his research aims at combining the state-of-art machine learning approaches with novel sensing technique to better support natural human-computer interaction. [Personal Website][LinkedIn]