# Real-Time Radar-Based Gesture Detection and Recognition Built in an Edge-Computing Platform

arXiv: https://arxiv.org/abs/2005.10145 · HTML: https://mlsanity.com/paper/2005.10145/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 May 2020.

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

- [Babel: A Scalable Pre-trained Model for Multi-Modal Sensing via Expandable Modality Alignment](https://mlsanity.com/paper/2407.17777/) (2024)
- [Resonate-and-Fire Spiking Neurons for Target Detection and Hand Gesture Recognition: A Hybrid Approach](https://mlsanity.com/paper/2405.19351/) (2024)
- [Filling the Pareto-Optimal Front for Affordance Segmentation on Embedded Devices Using RGB-D Cameras](https://mlsanity.com/paper/2607.28293/) (2026)
- [Lightweight Neural Networks for Affordance Segmentation: Enhancement of the Decoder Module](https://mlsanity.com/paper/2607.29473/) (2026)

## Abstract (arXiv)

In this paper, a real-time signal processing frame-work based on a 60 GHz frequency-modulated continuous wave (FMCW) radar system to recognize gestures is proposed. In order to improve the robustness of the radar-based gesture recognition system, the proposed framework extracts a comprehensive hand profile, including range, Doppler, azimuth and elevation, over multiple measurement-cycles and encodes them into a feature cube. Rather than feeding the range-Doppler spectrum sequence into a deep convolutional neural network (CNN) connected with recurrent neural networks, the proposed framework takes the aforementioned feature cube as input of a shallow CNN for gesture recognition to reduce the computational complexity. In addition, we develop a hand activity detection (HAD) algorithm to automatize the detection of gestures in real-time case. The proposed HAD can capture the time-stamp at which a gesture finishes and feeds the hand profile of all the relevant measurement-cycles before this time-stamp into the CNN with low latency. Since the proposed framework is able to detect and classify gestures at limited computational cost, it could be deployed in an edge-computing platform for real-time applications, whose performance is notedly inferior to a state-of-the-art personal computer. The experimental results show that the proposed framework has the capability of classifying 12 gestures in real-time with a high F1-score.