TIMMi: Finger-worn Textile Input Device with Multimodal Sensing in Mobile Interaction

by | Dec 2, 2014

Authors: Sang Ho Yoon, Vinh Nguyen, Ke Huo, Karthik Ramani
In Proceedings of the 9th International Conference on Tangible, Embedded, and Embodied Interaction (TEI'15), Stanford University, USA, 2015

Abstract: We introduce TIMMi, a textile input device for mobile interactions. TIMMi is worn on the index finger to provide a multimodal sensing input metaphor. The prototype is fabricated on a single layer of textile where the conductive silicone rubber is painted and the conductive threads are stitched. The sensing area comprises of three equally spaced dots and a separate wide line. Strain and pressure values are extracted from the line and three dots, respectively via voltage dividers. Regression analysis is performed to model the relationship between sensing values and finger pressure and bending. A multi-level thresholding is applied to capture different levels of finger bending and pressure. A temporal position tracking algorithm is implemented to capture the swipe gesture. In this preliminary study, we demonstrate TIMMi as a finger-worn input device with two applications: controlling music player and interacting with smartglasses.



Sang Ho Yoon

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]