Doppler-Radar Based Hand Gesture Recognition System Using Convolutional Neural Networks

TL;DR

This paper presents a Doppler radar-based hand gesture recognition system utilizing convolutional neural networks, achieving high accuracy and functioning effectively in dark environments, addressing limitations of camera-based systems.

Contribution

The study introduces a cost-effective Doppler radar sensor combined with CNNs for gesture recognition, demonstrating high accuracy and robustness in various conditions.

Findings

Achieved 98% recognition accuracy

Effective in dark and low-light conditions

Analyzed factors like distance and gesture scale

Abstract

Hand gesture recognition has long been a hot topic in human computer interaction. Traditional camera-based hand gesture recognition systems cannot work properly under dark circumstances. In this paper, a Doppler Radar based hand gesture recognition system using convolutional neural networks is proposed. A cost-effective Doppler radar sensor with dual receiving channels at 5.8GHz is used to acquire a big database of four standard gestures. The received hand gesture signals are then processed with time-frequency analysis. Convolutional neural networks are used to classify different gestures. Experimental results verify the effectiveness of the system with an accuracy of 98%. Besides, related factors such as recognition distance and gesture scale are investigated.