ArticleSensors (Basel, Switzerland)2026
Wavelet-Based Spectral Feature Gating for Near-Infrared Remote Photoplethysmography Estimation.
Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.