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

TL;DR

ThumbTrak is a wearable ring device that accurately recognizes 12 micro-finger poses in real-time using proximity sensors and machine learning, enabling nuanced hand gesture detection for interactive applications.

Contribution

It introduces a novel ring-based wearable with proximity sensors and an SVM model for real-time recognition of micro-finger poses, advancing gesture recognition technology.

Findings

Achieved 93.6% average accuracy in pose recognition

Successfully classified 12 micro-finger poses in real-time

Demonstrated potential for real-world gesture interaction

Abstract

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.