ThumbTrak: Recognizing Micro-finger Poses Using a Ring with Proximity Sensing
This addresses the problem of enabling subtle, hands-free input for users in human-computer interaction, though it is incremental as it builds on existing sensing and classification methods.
The paper tackled the problem of recognizing micro-finger poses for wearable input by developing ThumbTrak, a ring with proximity sensors, achieving 93.6% accuracy in real-time recognition of 12 poses in a user study.
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.