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Whitening-Aided Learning from Radar Micro-Doppler Signatures for Human Activity Recognition †

Deep learning architectures are being increasingly adopted for human activity recognition using radar technology. A majority of these architectures are based on convolutional neural networks (CNNs) and accept radar micro-Doppler signatures as input. The state-of-the-art CNN-based models employ batch...

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Autores principales: Sadeghi Adl, Zahra, Ahmad, Fauzia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10490630/
https://www.ncbi.nlm.nih.gov/pubmed/37687942
http://dx.doi.org/10.3390/s23177486
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author Sadeghi Adl, Zahra
Ahmad, Fauzia
author_facet Sadeghi Adl, Zahra
Ahmad, Fauzia
author_sort Sadeghi Adl, Zahra
collection PubMed
description Deep learning architectures are being increasingly adopted for human activity recognition using radar technology. A majority of these architectures are based on convolutional neural networks (CNNs) and accept radar micro-Doppler signatures as input. The state-of-the-art CNN-based models employ batch normalization (BN) to optimize network training and improve generalization. In this paper, we present whitening-aided CNN models for classifying human activities with radar sensors. We replace BN layers in a CNN model with whitening layers, which is shown to improve the model’s accuracy by not only centering and scaling activations, similar to BN, but also decorrelating them. We also exploit the rotational freedom afforded by whitening matrices to align the whitened activations in the latent space with the corresponding activity classes. Using real data measurements of six different activities, we show that whitening provides superior performance over BN in terms of classification accuracy for a CNN-based classifier. This demonstrates the potential of whitening-aided CNN models to provide enhanced human activity recognition with radar sensors.
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spelling pubmed-104906302023-09-09 Whitening-Aided Learning from Radar Micro-Doppler Signatures for Human Activity Recognition † Sadeghi Adl, Zahra Ahmad, Fauzia Sensors (Basel) Article Deep learning architectures are being increasingly adopted for human activity recognition using radar technology. A majority of these architectures are based on convolutional neural networks (CNNs) and accept radar micro-Doppler signatures as input. The state-of-the-art CNN-based models employ batch normalization (BN) to optimize network training and improve generalization. In this paper, we present whitening-aided CNN models for classifying human activities with radar sensors. We replace BN layers in a CNN model with whitening layers, which is shown to improve the model’s accuracy by not only centering and scaling activations, similar to BN, but also decorrelating them. We also exploit the rotational freedom afforded by whitening matrices to align the whitened activations in the latent space with the corresponding activity classes. Using real data measurements of six different activities, we show that whitening provides superior performance over BN in terms of classification accuracy for a CNN-based classifier. This demonstrates the potential of whitening-aided CNN models to provide enhanced human activity recognition with radar sensors. MDPI 2023-08-28 /pmc/articles/PMC10490630/ /pubmed/37687942 http://dx.doi.org/10.3390/s23177486 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Sadeghi Adl, Zahra
Ahmad, Fauzia
Whitening-Aided Learning from Radar Micro-Doppler Signatures for Human Activity Recognition †
title Whitening-Aided Learning from Radar Micro-Doppler Signatures for Human Activity Recognition †
title_full Whitening-Aided Learning from Radar Micro-Doppler Signatures for Human Activity Recognition †
title_fullStr Whitening-Aided Learning from Radar Micro-Doppler Signatures for Human Activity Recognition †
title_full_unstemmed Whitening-Aided Learning from Radar Micro-Doppler Signatures for Human Activity Recognition †
title_short Whitening-Aided Learning from Radar Micro-Doppler Signatures for Human Activity Recognition †
title_sort whitening-aided learning from radar micro-doppler signatures for human activity recognition †
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10490630/
https://www.ncbi.nlm.nih.gov/pubmed/37687942
http://dx.doi.org/10.3390/s23177486
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