Deep Learning for Enhanced Scratch Input

TL;DR

This paper presents a deep learning-based system that accurately recognizes scratch and tap gestures on various surfaces using only smartphones or tablets, eliminating the need for specialized hardware.

Contribution

It introduces a deep learning approach for scratch input that achieves high accuracy across diverse surfaces and environments without custom sensors.

Findings

Gesture classification accuracy of 95.8%

Effective across various surfaces and noise conditions

No need for specialized hardware

Abstract

The vibrations generated from scratching and tapping on surfaces can be highly expressive and recognizable, and have therefore been proposed as a method of natural user interface (NUI). Previous systems require custom sensor hardware such as contact microphones and have struggled with gesture classification accuracy. We propose a deep learning approach to scratch input. Using smartphones and tablets laid on tabletops or other similar surfaces, our system achieved a gesture classification accuracy of 95.8\%, substantially reducing gesture misclassification from previous works. Further, our system achieved this performance when tested on a wide variety of surfaces, mobile devices, and in high noise environments. The results indicate high potential for the application of deep learning techniques to natural user interface (NUI) systems that can readily convert large unpowered surfaces into a user interface using just a smartphone with no special-purpose sensors or hardware.