Ambient Light Based Hand Gesture Recognition Enabled by Recurrent Neural Network

Abstract

As an essential requirement of pervasive smart devices, free hand gestural input considered as necessary for user interactions has attracted lots of research attention for nearly decades. Nevertheless, existing proposals heavily rely on either expensive pre-deployed equipment or user on-body sensors, thus confine their application scenarios. In this paper, we propose a novel hand gesture recognition system which purely relies on ubiquitous ambient light and low-cost photodiodes. The proposed system does not need any modification to existing lighting infrastructure. While without complex signal pre-processing for modulated light, very low-cost photodiodes and processors can capture and process the light variations caused by hand gesture. To produce accurate hand gesture recognition, we design efficient algorithms based on recurrent neural network to process sensing data collected by a photodiode array. We implement a prototype consisting of an array of 8 photodiodes and extensive experiments demonstrate that the proposed solution can achieve a very high overall recognition accuracy of 99.31%.