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

arXiv: https://arxiv.org/abs/1711.02254 · HTML: https://mlsanity.com/paper/1711.02254/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Nov 2017.

## Built on by (most cited first, 4 of 4)

- [Radar-Camera Fusion for Object Detection and Semantic Segmentation in Autonomous Driving: A Comprehensive Review](https://mlsanity.com/paper/2304.10410/) (2023)
- [Exploring Radar Data Representations in Autonomous Driving: A Comprehensive Review](https://mlsanity.com/paper/2312.04861/) (2023)
- [A MIMO Radar-Based Metric Learning Approach for Activity Recognition](https://mlsanity.com/paper/2111.01939/) (2021)
- [mm-Wave Radar Hand Shape Classification Using Deformable Transformers](https://mlsanity.com/paper/2210.13079/) (2022)

## Abstract (arXiv)

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.