{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T15:05:42Z","timestamp":1777734342107,"version":"3.51.4"},"reference-count":35,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,3,14]],"date-time":"2023-03-14T00:00:00Z","timestamp":1678752000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF-2021R1F1A1052460"],"award-info":[{"award-number":["NRF-2021R1F1A1052460"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["IITP-2022-RS-2022-00156225"],"award-info":[{"award-number":["IITP-2022-RS-2022-00156225"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF-2017R1A5A1015596"],"award-info":[{"award-number":["NRF-2017R1A5A1015596"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["Research Resettlement Fund 2021"],"award-info":[{"award-number":["Research Resettlement Fund 2021"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"name":"MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center)","award":["NRF-2021R1F1A1052460"],"award-info":[{"award-number":["NRF-2021R1F1A1052460"]}]},{"name":"MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center)","award":["IITP-2022-RS-2022-00156225"],"award-info":[{"award-number":["IITP-2022-RS-2022-00156225"]}]},{"name":"MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center)","award":["NRF-2017R1A5A1015596"],"award-info":[{"award-number":["NRF-2017R1A5A1015596"]}]},{"name":"MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center)","award":["Research Resettlement Fund 2021"],"award-info":[{"award-number":["Research Resettlement Fund 2021"]}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF-2021R1F1A1052460"],"award-info":[{"award-number":["NRF-2021R1F1A1052460"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["IITP-2022-RS-2022-00156225"],"award-info":[{"award-number":["IITP-2022-RS-2022-00156225"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF-2017R1A5A1015596"],"award-info":[{"award-number":["NRF-2017R1A5A1015596"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["Research Resettlement Fund 2021"],"award-info":[{"award-number":["Research Resettlement Fund 2021"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002643","name":"Kwangwoon University","doi-asserted-by":"publisher","award":["NRF-2021R1F1A1052460"],"award-info":[{"award-number":["NRF-2021R1F1A1052460"]}],"id":[{"id":"10.13039\/501100002643","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002643","name":"Kwangwoon University","doi-asserted-by":"publisher","award":["IITP-2022-RS-2022-00156225"],"award-info":[{"award-number":["IITP-2022-RS-2022-00156225"]}],"id":[{"id":"10.13039\/501100002643","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002643","name":"Kwangwoon University","doi-asserted-by":"publisher","award":["NRF-2017R1A5A1015596"],"award-info":[{"award-number":["NRF-2017R1A5A1015596"]}],"id":[{"id":"10.13039\/501100002643","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002643","name":"Kwangwoon University","doi-asserted-by":"publisher","award":["Research Resettlement Fund 2021"],"award-info":[{"award-number":["Research Resettlement Fund 2021"]}],"id":[{"id":"10.13039\/501100002643","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Vital signs provide important biometric information for managing health and disease, and it is important to monitor them for a long time in a daily home environment. To this end, we developed and evaluated a deep learning framework that estimates the respiration rate (RR) and heart rate (HR) in real time from long-term data measured during sleep using a contactless impulse radio ultrawide-band (IR-UWB) radar. The clutter is removed from the measured radar signal, and the position of the subject is detected using the standard deviation of each radar signal channel. The 1D signal of the selected UWB channel index and the 2D signal applied with the continuous wavelet transform are entered as inputs into the convolutional neural-network-based model that then estimates RR and HR. From 30 recordings measured during night-time sleep, 10 were used for training, 5 for validation, and 15 for testing. The average mean absolute errors for RR and HR were 2.67 and 4.78, respectively. The performance of the proposed model was confirmed for long-term data, including static and dynamic conditions, and it is expected to be used for health management through vital-sign monitoring in the home environment.<\/jats:p>","DOI":"10.3390\/s23063116","type":"journal-article","created":{"date-parts":[[2023,3,15]],"date-time":"2023-03-15T05:22:59Z","timestamp":1678857779000},"page":"3116","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["Convolutional Neural Networks for the Real-Time Monitoring of Vital Signs Based on Impulse Radio Ultrawide-Band Radar during Sleep"],"prefix":"10.3390","volume":"23","author":[{"given":"Sang Ho","family":"Choi","sequence":"first","affiliation":[{"name":"School of Computer and Information Engineering, Kwangwoon University, Seoul 01897, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5397-5245","authenticated-orcid":false,"given":"Heenam","family":"Yoon","sequence":"additional","affiliation":[{"name":"Department of Human-Centered Artificial Intelligence, Sangmyung University, Seoul 03016, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"774","DOI":"10.1109\/JSEN.2018.2878607","article-title":"Detection of Breathing and Heart Rates in UWB Radar Sensor Data Using FVPIEF-Based Two-Layer EEMD","volume":"19","author":"Shyu","year":"2018","journal-title":"IEEE Sens. J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"38786","DOI":"10.1109\/ACCESS.2020.2976104","article-title":"Harmonic multiple loop detection (HMLD) algorithm for not-contact vital sign monitoring based on ultra-wideband (UWB) radar","volume":"8","author":"Zhang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"54958","DOI":"10.1109\/ACCESS.2019.2912956","article-title":"Remote Monitoring of Human Vital Signs Using mm-Wave FMCW Radar","volume":"7","author":"Alizadeh","year":"2019","journal-title":"IEEE Access"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3519","DOI":"10.1109\/TMTT.2017.2658567","article-title":"Comparison Study of Noncontact Vital Signs Detection Using a Doppler Stepped-Frequency Continuous-Wave Radar and Camera-Based Imaging Photoplethysmography","volume":"65","author":"Ren","year":"2017","journal-title":"IEEE Trans. Microw. Theory Tech."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Choi, J.W., Kim, D.H., Koo, D.L., Park, Y., Nam, H., Lee, J.H., Kim, H.J., Hong, S.-N., Jang, G., and Lim, S. (2022). Automated Detection of Sleep Apnea-Hypopnea Events Based on 60 GHz Frequency-Modulated Continuous-Wave Radar Using Convolutional Recurrent Neural Networks: A Preliminary Report of a Prospective Cohort Study. Sensors, 22.","DOI":"10.3390\/s22197177"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"5261","DOI":"10.1038\/s41598-020-62061-4","article-title":"Non-contact diagnosis of obstructive sleep apnea using impulse-radio ultra-wideband radar","volume":"10","author":"Kang","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"17556","DOI":"10.1109\/ACCESS.2021.3081747","article-title":"Hybrid CNN-LSTM network for real-time apnea-hypopnea event detection based on IR-UWB radar","volume":"10","author":"Kwon","year":"2021","journal-title":"IEEE Access"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"10300","DOI":"10.1109\/ACCESS.2016.2647226","article-title":"Multi-human detection algorithm based on an impulse radio ultra-wideband radar system","volume":"4","author":"Choi","year":"2016","journal-title":"IEEE Access"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"102475","DOI":"10.1016\/j.adhoc.2021.102475","article-title":"Human tracking and identification through a millimeter wave radar","volume":"116","author":"Zhao","year":"2021","journal-title":"Ad Hoc Netw."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"5717","DOI":"10.1109\/JSEN.2017.2723766","article-title":"People Counting Based on an IR-UWB Radar Sensor","volume":"17","author":"Choi","year":"2017","journal-title":"IEEE Sens. J."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"686","DOI":"10.1109\/TVT.2018.2883810","article-title":"A Novel NLOS Mitigation Algorithm for UWB Localization in Harsh Indoor Environments","volume":"68","author":"Yu","year":"2018","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_12","first-page":"1","article-title":"UWB Localization in a Smart Factory: Augmentation Methods and Experimental Assessment","volume":"70","author":"Barbieri","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"58148","DOI":"10.1109\/ACCESS.2019.2914410","article-title":"Hand pointing gestures based digital menu board implementation using IR-UWB transceivers","volume":"7","author":"Ghaffar","year":"2019","journal-title":"IEEE Access"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1066","DOI":"10.1109\/TIM.2019.2909249","article-title":"Detecting mid-air gestures for digit writing with radio sensors and a CNN","volume":"69","author":"Leem","year":"2019","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1109\/TBME.1985.325454","article-title":"A Microcprocessor-Based Noninvasive Arterial PulseWave Analyzer","volume":"32","author":"Lee","year":"1985","journal-title":"IEEE Trans. Biomed. Eng. BME"},{"key":"ref_16","unstructured":"Sharpe, S.M., Seals, J., MacDonald, A.H., and Crowgey, S.R. (1990). Non-Contact Vital Signs Monitor. (Application No. 4,958,638), U.S. Patent."},{"key":"ref_17","unstructured":"McEwan, T.E. (1994). Ultra-Wideband Radar Motion Sensor. (Application No. 5,361,070), U.S. Patent."},{"key":"ref_18","unstructured":"Berry, R.B., Albertario, C.L., Harding, S.M., Lloyd, R.B., Plante, D.T., Quan, S.F., Troester, M.M., and Vaughn, B.V. (2018). . The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specifications, Version 2.5, American Academy of Sleep Medicine."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"265","DOI":"10.2528\/PIER09120302","article-title":"Analysis of vital signs monitoring using an IR-UWB radar","volume":"100","author":"Lazaro","year":"2010","journal-title":"Prog. Electromagn. Res."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2595","DOI":"10.3390\/s140202595","article-title":"Techniques for clutter suppression in the presence of body movements during the detection of respiratory activity through UWB radars","volume":"14","author":"Lazaro","year":"2014","journal-title":"Sensors"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1627","DOI":"10.1021\/ac60214a047","article-title":"Smoothing and differentiation of data by simplified least squares procedures","volume":"36","author":"Savitzky","year":"1964","journal-title":"Anal. Chem."},{"key":"ref_22","unstructured":"Sapra, A., Malik, A., and Bhandari, P. (2022). Vital Sign Assessment, StatPearls Publishing."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"723","DOI":"10.1137\/0515056","article-title":"Decomposition of Hardy functions into square integrable wavelets of constant shape","volume":"15","author":"Grossmann","year":"1984","journal-title":"SIAM J. Math. Anal."},{"key":"ref_24","unstructured":"Sergey, I., and Szegedy, C. (2015, January 7\u20139). Batch normalization: Accelerating deep network training by reducing internal covariate shift. Proceedings of the International Conference on Machine Learning, Lille, France."},{"key":"ref_25","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Smith, L.N. (2017, January 24\u201331). Cyclical learning rates for training neural networks. Proceedings of the 2017 IEEE Winter Conference on Applications of Computer Vision (WACV), Santa Rosa, CA, USA.","DOI":"10.1109\/WACV.2017.58"},{"key":"ref_27","unstructured":"Falcon, W., Borovec, J., Schock, J., W\u00e4lchli, A., Mocholi, A., Nitta, A., Chaton, T., Harris, E., Skafte, N., and Jordan, J. (2022, December 20). Pytorch Lightning. Available online: https:\/\/github.com\/PytorchLightning\/pytorch-lightning."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"36888","DOI":"10.1109\/ACCESS.2018.2886825","article-title":"Non-contact detection of vital signs using a UWB radar sensor","volume":"7","author":"Duan","year":"2018","journal-title":"IEEE Access"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"3319","DOI":"10.1109\/TMTT.2016.2597824","article-title":"Phase-Based Methods for Heart Rate Detection Using UWB Impulse Doppler Radar","volume":"64","author":"Ren","year":"2016","journal-title":"IEEE Trans. Microw. Theory Tech."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"El-Bardan, R., Malaviya, D., and Di Rienzo, A. (2017, January 13\u201315). On the estimation of respiration and heart rates via an IR-UWB radar: An algorithmic perspective. Proceedings of the 2017 IEEE International Conference on Microwaves, Antennas, Communications and Electronic Systems (COMCAS), Tel-Aviv, Israel.","DOI":"10.1109\/COMCAS.2017.8244781"},{"key":"ref_31","unstructured":"Nguyen, V., Javaid, A.Q., and Weitnauer, M.A. (2014, January 26\u201330). Spectrum-averaged Harmonic Path (SHAPA) algorithm for non-contact vital sign monitoring with ultra-wideband (UWB) radar. Proceedings of the 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Chicago, IL, USA."},{"key":"ref_32","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., and Van Der Maaten, L. (2017, January 21\u201326). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_35","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2010). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv, preprint."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/6\/3116\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:55:07Z","timestamp":1760122507000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/6\/3116"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,14]]},"references-count":35,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["s23063116"],"URL":"https:\/\/doi.org\/10.3390\/s23063116","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,14]]}}}