{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T13:06:12Z","timestamp":1789995972843,"version":"4.0.1"},"reference-count":54,"publisher":"Institution of Engineering and Technology (IET)","issue":"1","license":[{"start":{"date-parts":[[2026,5,10]],"date-time":"2026-05-10T00:00:00Z","timestamp":1778371200000},"content-version":"vor","delay-in-days":129,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["ietresearch.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Healthcare Tech Letters"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Photoplethysmography (PPG) enables non\u2010invasive estimation of vital signs across a range of sensors, from wrist wearables and clinical monitors to smartphone cameras. We present parameter\u2010efficient, end\u2010to\u2010end deep models for heart rate (HR), blood oxygen saturation () and respiratory rate (RR) estimation. These models operate directly on PPG signals and integrate preprocessing as a layer. Our family of fully convolutional architectures includes a baseline FCN, a residual FCN, a ConvNeXt\u2010inspired FCN and a compact DCT\u2010based variant. These models achieve strong cross\u2010dataset performance while using only 3.3K\u201353K parameters. On PPG\u2010DaLiA, the best performing model achieves 6.07  2.70 bpm MAE for HR. In HR,  and RR tasks on the BIDMC dataset, the best\u2010performing method achieves error rates of 2.2 bpm, 3.14% and 1.49 breaths per minute, respectively. We also release MTHS, a smartphone\u2010based vital\u2010sign dataset comprising 62 fingertip\u2010PPG recordings (35 men and 27 women) with 1\u2010Hz HR and  annotations. Additionally, we demonstrate practical feasibility via on\u2010device deployment using TFLite on smartphones. These results show that compact, fully convolutional and DCT\u2010based designs provide accurate, generalizable, and deployment\u2010ready PPG vital\u2010sign estimators across sensors and\u00a0datasets.<\/jats:p>","DOI":"10.1049\/htl2.70083","type":"journal-article","created":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T04:57:26Z","timestamp":1778475446000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Parameter\u2010Efficient Deep Learning Models for Vital Sign Estimation From PPG"],"prefix":"10.1049","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-7023-0377","authenticated-orcid":false,"given":"Taha","family":"Samavati","sequence":"first","affiliation":[{"name":"Department of Computer Engineering Iran University of Science and Technology Tehran Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-9454-7198","authenticated-orcid":false,"given":"Mahdi","family":"Farvardin","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering Iran University of Science and Technology Tehran Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aboozar","family":"Ghaffari","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering Iran University of Science and Technology Tehran Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"265","published-online":{"date-parts":[[2026,5,10]]},"reference":[{"issue":"4","key":"e_1_2_17_2_1","first-page":"195","article-title":"A Review on Wearable Photoplethysmography Sensors and Their Potential Future Applications in Health Care","volume":"4","author":"Castaneda D.","year":"2018","journal-title":"International Journal of Biosensors & Bioelectronics"},{"key":"e_1_2_17_3_1","unstructured":"C.ChourpiliadisandA.Bhardwaj \u201cPhysiology Respiratory Rate \u201d inStatPearls [Internet](StatPearls Publishing 2024) https:\/\/www.ncbi.nlm.nih.gov\/books\/NBK537306\/."},{"key":"e_1_2_17_4_1","doi-asserted-by":"publisher","DOI":"10.1164\/ajrccm.167.10.950"},{"key":"e_1_2_17_5_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12890-015-0003-5"},{"key":"e_1_2_17_6_1","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2020.573037"},{"key":"e_1_2_17_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0954-6111(96)90017-7"},{"key":"e_1_2_17_8_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10916-016-0485-6"},{"key":"e_1_2_17_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2018.2887209"},{"key":"e_1_2_17_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2020.101928"},{"key":"e_1_2_17_11_1","doi-asserted-by":"crossref","unstructured":"A. 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