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Classification of human body motions using an ultra-wideband pulse radar

BACKGROUND: The motion or gestures of a person are primarily recognized by detecting a specific object and the change in its position from image information obtained via an image sensor. However, the use of such systems is limited due to privacy concerns. OBJECTIVE: To overcome these concerns, this...

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Detalles Bibliográficos
Autores principales: Cho, Hui-Sup, Park, Young-Jin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: IOS Press 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8842765/
https://www.ncbi.nlm.nih.gov/pubmed/34092669
http://dx.doi.org/10.3233/THC-212827
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author Cho, Hui-Sup
Park, Young-Jin
author_facet Cho, Hui-Sup
Park, Young-Jin
author_sort Cho, Hui-Sup
collection PubMed
description BACKGROUND: The motion or gestures of a person are primarily recognized by detecting a specific object and the change in its position from image information obtained via an image sensor. However, the use of such systems is limited due to privacy concerns. OBJECTIVE: To overcome these concerns, this study proposes a radar-based motion recognition method. METHODS: Detailed human body movement data were generated using ultra-wideband (UWB) radar pulses, which provide precise spatial resolution. The pulses reflected from the body were stacked to reveal the body’s movements and these movements were expressed in detail in the micro-range components. The collected radar data with emphasized micro-ranges were converted into an image. Convolutional neural networks (CNN) trained on radar images for various motions were used to classify specific motions. Instead of training the CNNs from scratch, transfer learning is performed by importing pretrained CNNs and fine-tuning their parameters with the radar images. Three pretrained CNNs, Resnet18, Resnet101, and Inception-Resnet-V2, were retrained under various training conditions and their performance was experimentally verified. RESULTS: As a result of various experiments, we conclude that detailed motions of subjects can be accurately classified by utilizing CNNs that were retrained with images obtained from the UWB pulse radar.
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spelling pubmed-88427652022-03-02 Classification of human body motions using an ultra-wideband pulse radar Cho, Hui-Sup Park, Young-Jin Technol Health Care Research Article BACKGROUND: The motion or gestures of a person are primarily recognized by detecting a specific object and the change in its position from image information obtained via an image sensor. However, the use of such systems is limited due to privacy concerns. OBJECTIVE: To overcome these concerns, this study proposes a radar-based motion recognition method. METHODS: Detailed human body movement data were generated using ultra-wideband (UWB) radar pulses, which provide precise spatial resolution. The pulses reflected from the body were stacked to reveal the body’s movements and these movements were expressed in detail in the micro-range components. The collected radar data with emphasized micro-ranges were converted into an image. Convolutional neural networks (CNN) trained on radar images for various motions were used to classify specific motions. Instead of training the CNNs from scratch, transfer learning is performed by importing pretrained CNNs and fine-tuning their parameters with the radar images. Three pretrained CNNs, Resnet18, Resnet101, and Inception-Resnet-V2, were retrained under various training conditions and their performance was experimentally verified. RESULTS: As a result of various experiments, we conclude that detailed motions of subjects can be accurately classified by utilizing CNNs that were retrained with images obtained from the UWB pulse radar. IOS Press 2021-12-29 /pmc/articles/PMC8842765/ /pubmed/34092669 http://dx.doi.org/10.3233/THC-212827 Text en © 2022 – The authors. Published by IOS Press. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution Non-Commercial (CC BY-NC 4.0) License (https://creativecommons.org/licenses/by-nc/4.0/) , which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Cho, Hui-Sup
Park, Young-Jin
Classification of human body motions using an ultra-wideband pulse radar
title Classification of human body motions using an ultra-wideband pulse radar
title_full Classification of human body motions using an ultra-wideband pulse radar
title_fullStr Classification of human body motions using an ultra-wideband pulse radar
title_full_unstemmed Classification of human body motions using an ultra-wideband pulse radar
title_short Classification of human body motions using an ultra-wideband pulse radar
title_sort classification of human body motions using an ultra-wideband pulse radar
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8842765/
https://www.ncbi.nlm.nih.gov/pubmed/34092669
http://dx.doi.org/10.3233/THC-212827
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