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.

Authors: Taizhou Chen, Tianpei Li, Xingyu Yang, and Kening Zhu