Hand Gesture Recognition from Doppler Radar Signals Using Echo State Networks

TL;DR

This paper introduces an Echo State Network-based method for hand gesture recognition using Doppler radar signals, achieving high accuracy with low computational cost, suitable for resource-limited human-computer interaction applications.

Contribution

The paper presents a novel ESN-based approach for radar-based HGR that outperforms existing deep learning methods in accuracy and efficiency.

Findings

Outperforms existing methods on Soli and Dop-NET datasets.

Achieves high recognition accuracy with low computational cost.

Effective for recognizing temporal patterns in radar signals.

Abstract

Hand gesture recognition (HGR) is a fundamental technology in human computer interaction (HCI).In particular, HGR based on Doppler radar signals is suited for in-vehicle interfaces and robotic systems, necessitating lightweight and computationally efficient recognition techniques. However, conventional deep learning-based methods still suffer from high computational costs. To address this issue, we propose an Echo State Network (ESN) approach for radar-based HGR, using frequency-modulated-continuous-wave (FMCW) radar signals. Raw radar data is first converted into feature maps, such as range-time and Doppler-time maps, which are then fed into one or more recurrent neural network-based reservoirs. The obtained reservoir states are processed by readout classifiers, including ridge regression, support vector machines, and random forests. Comparative experiments demonstrate that our method outperforms existing approaches on an 11-class HGR task using the Soli dataset and surpasses existing deep learning models on a 4-class HGR task using the Dop-NET dataset. The results indicate that parallel processing using multi-reservoir ESNs are effective for recognizing temporal patterns from the multiple different feature maps in the time-space and time-frequency domains. Our ESN approaches achieve high recognition performance with low computational cost in HGR, showing great potential for more advanced HCI technologies, especially in resource-constrained environments.