{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,5]],"date-time":"2026-09-05T16:09:01Z","timestamp":1788624541931,"version":"build-2803163510"},"reference-count":11,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2021,12,29]],"date-time":"2021-12-29T00:00:00Z","timestamp":1640736000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["THC"],"published-print":{"date-parts":[[2021,12,29]]},"abstract":"<jats:p>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\u2019s 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.<\/jats:p>","DOI":"10.3233\/thc-212827","type":"journal-article","created":{"date-parts":[[2021,6,1]],"date-time":"2021-06-01T13:18:39Z","timestamp":1622553519000},"page":"93-104","source":"Crossref","is-referenced-by-count":9,"title":["Classification of human body motions using an ultra-wideband pulse radar"],"prefix":"10.1177","volume":"30","author":[{"given":"Hui-Sup","family":"Cho","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Young-Jin","family":"Park","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","reference":[{"key":"10.3233\/THC-212827_ref1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3233\/THC-202626","article-title":"Measurement of pulse transit time using ultra-wideband radar","author":"Cho","year":"2020","journal-title":"Technology and Health Care."},{"key":"10.3233\/THC-212827_ref2","doi-asserted-by":"crossref","first-page":"878","DOI":"10.1049\/el.2019.1438","article-title":"Monitoring heart activity using ultra-wideband radar","volume":"55","author":"Cho","year":"2019","journal-title":"Electronics Letters."},{"key":"10.3233\/THC-212827_ref3","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/LGRS.2015.2491329","article-title":"Human detection and activity classification based on micro-doppler signatures using deep convolutional neural networks","volume":"13","author":"Kim","year":"2016","journal-title":"IEEE Geoscience and Remote Sensing Letters."},{"key":"10.3233\/THC-212827_ref4","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1016\/j.matcom.2020.04.031","article-title":"Application of the residue number system to reduce hardware costs of the convolutional neural network implementation","volume":"177","author":"Valueva","year":"2020","journal-title":"Mathematics and Computers in Simulation."},{"key":"10.3233\/THC-212827_ref5","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren X, Sun J. Deep residual learning for image recognition, Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2016; 770-778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"10.3233\/THC-212827_ref6","doi-asserted-by":"crossref","unstructured":"Szegedy C, Ioffe S, Vanhoucke V, Alemi AA. Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning. Proceedings of the 31st AAAI Conference on Artificial Intelligence. 2017; 4278-4284.","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"10.3233\/THC-212827_ref7","doi-asserted-by":"crossref","unstructured":"Cristianini N, Shawe-Taylor J. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge Uinv. Press, 2000.","DOI":"10.1017\/CBO9780511801389"},{"key":"10.3233\/THC-212827_ref8","unstructured":"Goodfellow I, Bengio Y, Courville A. Deep Learning. The MIT Press; 2016. (ISBN: 978-0262035613)"},{"issue":"11","key":"10.3233\/THC-212827_ref9","doi-asserted-by":"crossref","first-page":"1472","DOI":"10.1016\/j.acra.2018.02.018","article-title":"Deep learning in radiology","volume":"25","author":"McBee","year":"2018","journal-title":"Academic Radiology."},{"key":"10.3233\/THC-212827_ref10","doi-asserted-by":"publisher","DOI":"10.1145\/3065386"},{"key":"10.3233\/THC-212827_ref11","doi-asserted-by":"crossref","first-page":"2352","DOI":"10.1162\/neco_a_00990","article-title":"Deep convolutional neural networks for image classification: A comprehensive review","volume":"29","author":"Rawat","year":"2017","journal-title":"Neural Computation."}],"container-title":["Technology and Health Care"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/THC-212827","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T05:53:14Z","timestamp":1777614794000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/THC-212827"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,29]]},"references-count":11,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.3233\/thc-212827","relation":{},"ISSN":["0928-7329","1878-7401"],"issn-type":[{"value":"0928-7329","type":"print"},{"value":"1878-7401","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12,29]]}}}