ArticleFrontiers in physiology2026
DeepRespNet: a hybrid attention-recurrent framework for non-contact respiratory rate estimation.
Article in Frontiers in physiology, 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
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: The noncontact measurement of respiratory rate (RR) has gained considerable attention recently due to its relevance to remote healthcare and continuous physiological monitoring. However, existing camera-based approaches often exhibit reduced accuracy in subjects having darker skin tones, primarily owing to melanin absorption and variations in illumination that degrade remote photoplethysmographic (rPPG) signal quality. This study aims to develop a robust deep learning framework that ensures reliable RR estimation across diverse skin tones. Methods: A hybrid deep learning framework, referred to as DeepRespNet, is proposed that jointly analyzes rPPG and motion signals extracted from facial and thoracic regions in RGB video sequences. The core feature encoder, termed RespFormer, integrates spatiotemporal convolution with multi-head self-attention to capture both local and long-range respiratory patterns. The learned representations are further processed by a separate bidirectional long short-term memory (BiLSTM) network to model temporal coherence and generate physiologically stable respiratory waveforms. Optical-flow-based motion features and rPPG color variations are combined to form a multi-channel respiratory representation. Results: The proposed framework was evaluated on a multi-subject dataset with synchronized reference respiration signals. Experimental results achieve mean absolute errors of 0.45 breaths per minute (BPM) for light-skinned subjects and 0.80 BPM for dark-skinned subjects. Bland-Altman and cross dataset analyses further confirm strong agreement and consistent performance across skin tone groups. Conclusion: The proposed framework enables reliable and skin-tone-aware noncontact respiratory rate estimation. Initial findings indicate its potential suitability for camera-based respiratory monitoring in remote healthcare and telemedicine applications, though further validation on larger populations is required.
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.