{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T05:05:56Z","timestamp":1789103156027,"version":"build-2803163510"},"reference-count":32,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T00:00:00Z","timestamp":1788912000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Institute of Information &amp; Communications Technology Planning &amp; Evaluation","award":["IITP-2026-RS-2026-25615995"],"award-info":[{"award-number":["IITP-2026-RS-2026-25615995"]}]},{"award":["IITP-2026-RS-2026-25615995"],"award-info":[{"award-number":["IITP-2026-RS-2026-25615995"]}],"id":[{"id":"https:\/\/ror.org\/01g0hqq23","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Near-infrared (NIR) video-based remote photoplethysmography (rPPG) enables physiological measurement under low-light conditions using monochrome facial videos. NIR videos lack chrominance-based cues, while weak cardiac variations remain entangled with motion, illumination, and imaging noise. To address these limitations, we propose a wavelet-based spectral feature gating (WSFG) module for NIR-based rPPG estimation. The WSFG module applies a two-level stationary wavelet transform (SWT) to decompose intermediate temporal features without temporal downsampling. Specifically, learnable spectral gates regulate each sub-band contribution and reconstruct branch-specific temporal features through inverse SWT. Three specialized branches preserve cardiac variations, suppress nuisance components, and model relative optical attenuation from low-frequency reference features. Experiments on egoPPG-DB dataset demonstrate that the proposed method achieves the lowest mean absolute error (MAE) and mean absolute percentage error (MAPE) of 8.56 bpm and 9.86%, respectively. Further analysis highlight the effectiveness of frequency-aware feature modeling for NIR-only rPPG estimation.<\/jats:p>","DOI":"10.3390\/s26185734","type":"journal-article","created":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T14:02:48Z","timestamp":1788962568000},"page":"5734","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Wavelet-Based Spectral Feature Gating for Near-Infrared Remote Photoplethysmography Estimation"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6365-2231","authenticated-orcid":false,"given":"Gyutae","family":"Hwang","sequence":"first","affiliation":[{"name":"Division of Electronic and Information Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9312-6299","authenticated-orcid":false,"given":"Sang Jun","family":"Lee","sequence":"additional","affiliation":[{"name":"Division of Electronic and Information Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1479","DOI":"10.1109\/TBME.2016.2609282","article-title":"Algorithmic principles of remote PPG","volume":"64","author":"Wang","year":"2016","journal-title":"IEEE Trans. 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