{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T00:48:56Z","timestamp":1760057336121,"version":"build-2065373602"},"reference-count":35,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2025,1,31]],"date-time":"2025-01-31T00:00:00Z","timestamp":1738281600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Funda\u00e7\u00e3o para a Ci\u00eancia e Tecnologia","award":["UIDB\/00127\/2020","10.54499\/UIDB\/00127\/2020","10.54499\/UIDP\/00127\/2020","10.54499\/DL57\/2016\/CP1482\/CT0096","2022.05005.PTDC","UIDB\/50008\/2020-UIDP\/50008\/2020 (IT)","EMPA 2022.05005.PTDC"],"award-info":[{"award-number":["UIDB\/00127\/2020","10.54499\/UIDB\/00127\/2020","10.54499\/UIDP\/00127\/2020","10.54499\/DL57\/2016\/CP1482\/CT0096","2022.05005.PTDC","UIDB\/50008\/2020-UIDP\/50008\/2020 (IT)","EMPA 2022.05005.PTDC"]}]},{"name":"national funds through FCT\u2014Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia, I.P.","award":["UIDB\/00127\/2020","10.54499\/UIDB\/00127\/2020","10.54499\/UIDP\/00127\/2020","10.54499\/DL57\/2016\/CP1482\/CT0096","2022.05005.PTDC","UIDB\/50008\/2020-UIDP\/50008\/2020 (IT)","EMPA 2022.05005.PTDC"],"award-info":[{"award-number":["UIDB\/00127\/2020","10.54499\/UIDB\/00127\/2020","10.54499\/UIDP\/00127\/2020","10.54499\/DL57\/2016\/CP1482\/CT0096","2022.05005.PTDC","UIDB\/50008\/2020-UIDP\/50008\/2020 (IT)","EMPA 2022.05005.PTDC"]}]},{"name":"European Regional Development Fund, FSE through COMPETE2020, through FCT","award":["UIDB\/00127\/2020","10.54499\/UIDB\/00127\/2020","10.54499\/UIDP\/00127\/2020","10.54499\/DL57\/2016\/CP1482\/CT0096","2022.05005.PTDC","UIDB\/50008\/2020-UIDP\/50008\/2020 (IT)","EMPA 2022.05005.PTDC"],"award-info":[{"award-number":["UIDB\/00127\/2020","10.54499\/UIDB\/00127\/2020","10.54499\/UIDP\/00127\/2020","10.54499\/DL57\/2016\/CP1482\/CT0096","2022.05005.PTDC","UIDB\/50008\/2020-UIDP\/50008\/2020 (IT)","EMPA 2022.05005.PTDC"]}]},{"name":"FCT\/Minist\u00e9rio da Ci\u00eancia, Tecnologia e Ensino Superior (MCTES)","award":["UIDB\/00127\/2020","10.54499\/UIDB\/00127\/2020","10.54499\/UIDP\/00127\/2020","10.54499\/DL57\/2016\/CP1482\/CT0096","2022.05005.PTDC","UIDB\/50008\/2020-UIDP\/50008\/2020 (IT)","EMPA 2022.05005.PTDC"],"award-info":[{"award-number":["UIDB\/00127\/2020","10.54499\/UIDB\/00127\/2020","10.54499\/UIDP\/00127\/2020","10.54499\/DL57\/2016\/CP1482\/CT0096","2022.05005.PTDC","UIDB\/50008\/2020-UIDP\/50008\/2020 (IT)","EMPA 2022.05005.PTDC"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>This work presents an innovative method for detecting the respiratory patterns of subjects walking on a treadmill, by leveraging the capabilities of deep learning (DL) technologies and a dual-radar setup. The study aims to overcome the challenge of accurately capturing respiratory rates in subjects performing body movements, a scenario less addressed in prior studies. By employing two radars operating at 5.8 GHz for motion mitigation, this study compares the efficacy of dual-radar configurations against a single-radar setup. The study employs DL algorithms based on convolutional autoencoders to mitigate the low-quality demodulated radar signals by reconstructing the respiratory signal. The models are trained with data from a single subject and data from 15 subjects, attaining average absolute errors of 0.29 and 4.59 Respiration Per Minute (RPM), respectively, allowing to conclude that the use of DL algorithms enhances the accuracy of respiratory signal detection when compared with arctangent demodulation, even in cases where radar data contain minimal information regarding vital signals.<\/jats:p>","DOI":"10.3390\/info16020099","type":"journal-article","created":{"date-parts":[[2025,1,31]],"date-time":"2025-01-31T09:16:26Z","timestamp":1738314986000},"page":"99","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Movement Compensation in Dual Continuous Wave Radar Using Deep Learning"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-2872-7911","authenticated-orcid":false,"given":"Gon\u00e7alo","family":"Gomes","sequence":"first","affiliation":[{"name":"IEETA, DETI, LASI, Universidade de Aveiro, 3810-193 Aveiro, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8650-9219","authenticated-orcid":false,"given":"Susana","family":"Br\u00e1s","sequence":"additional","affiliation":[{"name":"IEETA, DETI, LASI, Universidade de Aveiro, 3810-193 Aveiro, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2077-2871","authenticated-orcid":false,"given":"Carolina","family":"Gouveia","sequence":"additional","affiliation":[{"name":"AlmaScience Association\u2014Pulp Research and Development for Smart and Sustainable Applications Madan Parque, Rua dos Inventores, 2825-182 Caparica, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8897-9123","authenticated-orcid":false,"given":"Daniel","family":"Albuquerque","sequence":"additional","affiliation":[{"name":"\u00c1gueda School of Technology and Management, University of Aveiro, 3810-193 Aveiro, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5588-7794","authenticated-orcid":false,"given":"Pedro","family":"Pinho","sequence":"additional","affiliation":[{"name":"Instituto de Telecomunica\u00e7\u00f5es, Department of Electronics, Telecommunications and Informatics (DETI), University of Aveiro, 3810-193 Aveiro, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2025,1,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Mahomela, M.K., Owolawi, P.A., Mapayi, T., Malele, V., and Aiyetoro, G. 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