Detecting Thumb-Posture for One-handed Interactions with Smartphone using Acoustic Sensing

This paper presents a novel approach for expanding one-handed interactions using the thumb positioned above the smartphone screen. Our approach is based on acoustic sensing, a technique for leveraging the built-in speaker and microphone of the smartphone without requiring additional sensors or attachments. We explored the feasibility of our approach on smartphones with the conventional speaker and microphone arrangement and investigated the enhancement of recognition accuracy by using smartphones equipped with Acoustic Surface, which is a technology enabling the entire screen to vibrate and emit sound over a wider area and installed in several commercial smartphones such as LG G8 ThinQ and Huawei P30 Pro. We focused on classifying 12 different thumb postures and developed models that achieve prediction accuracies of 78.6% (conventional smartphone) and 87.0% (Acoustic Surface).