Affiliation:
1. School of Creative Media, City University of Hong Kong, Hong Kong, China
Abstract
We present EFRing, an index-finger-worn ring-form device for detecting thumb-to-index-finger (T2I) microgestures through the approach of electric-field (EF) sensing. Based on the signal change induced by the T2I motions, we proposed two machine-learning-based data-processing pipelines: one for recognizing/classifying discrete T2I microgestures, and the other for tracking continuous 1D T2I movements. Our experiments on the EFRing microgesture classification showed an average within-user accuracy of 89.5% and an average cross-user accuracy of 85.2%, for 9 discrete T2I microgestures. For the continuous tracking of 1D T2I movements, our method can achieve the mean-square error of 3.5% for the generic model and 2.3% for the personalized model. Our 1D-Fitts'-Law target-selection study shows that the proposed tracking method with EFRing is intuitive and accurate for real-time usage. Lastly, we proposed and discussed the potential applications for EFRing.
Funder
National Natural Science Foundation of China
CityU Contract Research_RMGS
CityU Donations for Research Projects_RMGS
Centre for Applied Computing and Interactive Media (ACIM) of School of Creative Media
Guangdong Basic and Applied Basic Research Foundation
Publisher
Association for Computing Machinery (ACM)
Subject
Computer Networks and Communications,Hardware and Architecture,Human-Computer Interaction
Cited by
4 articles.
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