Your Table Can Be an Input Panel

Abstract

This paper explores the possibility of extending the input and interactions beyond the small screen of the mobile device onto ad hoc adjacent surfaces, e.g., a wooden tabletop with acoustic signals. While the existing finger tracking approaches employ the active acoustic signal with a fixed frequency, our proposed system Ipanel employs the acoustic signals generated by sliding of fingers on the table for tracking. Different from active signal tracking, the frequency of the finger-table generated acoustic signals keeps changing, making accurate tracking much more challenging than the traditional approaches with fix frequency signal from the speaker. Unique features are extracted by exploiting the spatio-temporal and frequency domain properties of the generated acoustic signals. The features are transformed into images and then we employ the convolutional neural network (CNN) to recognize the finger movement on the table. Ipanel is able to support not only commonly used gesture (click, flip, scroll, zoom, etc.) recognition, but also handwriting (10 numbers and 26 alphabets) recognition at high accuracies. We implement Ipanel on smartphones, and conduct extensive real environment experiments to evaluate its performance. The results validate the robustness of Ipanel, and show that it maintains high accuracies across different users with varying input behaviours (e.g., input strength, speed and region). Further, Ipanel's performance is robust against different levels of ambient noise and varying surface materials.