Enhancing FMCW Radar Gesture Classification With Physically Interpretable Data Augmentation

Enhancing FMCW Radar Gesture Classification With Physically Interpretable Data Augmentation

This study introduces a novel, physically interpretable data augmentation framework that improves the robustness and accuracy of hand gesture recognition using Frequency-Modulated Continuous Wave (FMCW) radar and Convolutional Neural Networks (CNN). The proposed reconfigurable and parametric method...

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Bibliographic Details
Main Authors: Alessandra Fusco, Zain Amir Zaman, Souvik Hazra, Lorenzo Servadei, Robert Wille
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
60GHz radar
FMCW
data augmentation
hand gesture classification
machine learning
signal processing
Online Access:https://ieeexplore.ieee.org/document/10946155/
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https://ieeexplore.ieee.org/document/10946155/

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