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ThumbTrak: Recognizing Micro-finger Poses Using a Ring with Proximity Sensing

To appear on the ACM International Conference on Mobile Human-Computer Interaction (MobileHCI) ’21

Wei Sun, Franklin Mingzhe Li, Congshu Huang, Zhenyu LeiBenjamin Steeper, Songyun Tao, Feng Tian, Cheng Zhang


ThumbTrak is a novel wearable input device that recognizes 12 micro-finger poses in real-time. Poses are characterized by the thumb touching each of the 12 phalanges on the hand. It uses a thumb-ring, built with a flexible printed circuit board, which hosts nine proximity sensors. Each sensor measures the distance from the thumb to various parts of the palm or other fingers. ThumbTrak uses a support-vector-machine (SVM) model to classify finger poses based on distance measurements in real-time. A user study with ten participants showed that ThumbTrak could recognize 12 micro finger poses with an average accuracy of 93.6%. We also discuss potential opportunities and challenges in applying ThumbTrak in real-world applications.

Demo Video of ThumbTrak

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