SoundScroll: Robust Finger Slide Detection Using Friction Sound and Wrist-Worn Microphones

Smartwatches have firmly established themselves as a popular wearable form factor. The potential expansion of their interaction space to nearby surfaces offers a promising avenue for enhancing input accuracy and usability beyond the confines of a small screen. However, a key challenge is in detecting continuous contact states with the surface to inform the start and end of stateful interactions. In this paper, we introduce SoundScroll, enabling a rapid and precise determination of contact state and fingertip speed of sliding finger. We leverage vibrations from friction between a moving finger and a surface. Our proof-of-concept wristband captures a dual-channel vibration signal for robust sensing, considering both on-skin and in-air components. Our software predicts a finger sliding state as fast as 20 ms with an accuracy of 93.3%. Augmenting prior approaches detecting tap events, SoundScroll can be a robust, low-latency, and precise contact and motion sensing technique.