EFRing: Enabling Thumb-to-Index-Finger Microgesture Interaction through Electric Field Sensing Using Single Smart Ring

Taizhou Chen, Tianpei Li, Xingyu Yang, Kening Zhu
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引用次数: 4

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.
EFRing:使用单个智能环通过电场感应实现拇指到食指的微手势交互
我们提出了EFRing,一种食指佩戴的环形装置,用于通过电场(EF)传感方法检测拇指到食指(T2I)的微手势。基于T2I运动引起的信号变化,我们提出了两种基于机器学习的数据处理管道:一种用于识别/分类离散的T2I微手势,另一种用于跟踪连续的1D T2I运动。我们对EFRing微手势分类的实验表明,对于9个离散的T2I微手势,用户内平均准确率为89.5%,用户间平均准确率为85.2%。对于1D T2I运动的连续跟踪,我们的方法可以实现通用模型3.5%的均方误差和个性化模型2.3%的均方误差。我们的1D-Fitts定律目标选择研究表明,所提出的EFRing跟踪方法直观、准确,适合实时使用。最后,我们提出并讨论了EFRing的潜在应用。
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