FingerSound

Abstract

We introduce FingerSound, an input technology to recognize unistroke thumb gestures, which are easy to learn and can be performed through eyes-free interaction. The gestures are performed using a thumb-mounted ring comprising a contact microphone and a gyroscope sensor. A K-Nearest-Neighbor(KNN) model with a distance function of Dynamic Time Warping (DTW) is built to recognize up to 42 common unistroke gestures. A user study, where the real-time classification results were given, shows an accuracy of 92%-98% by a machine learning model built with only 3 training samples per gesture. Based on the user study results, we further discuss the opportunities, challenges and practical limitations of FingerSound when deploying it to real-world applications in the future.