ArticleSensors (Basel, Switzerland)2020
Non-Contact Heart Rate Detection When Face Information Is Missing during Online Learning.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
What it found
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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
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Continuous Monitoring of Vital Signs Using Cameras: A Systematic Review.Sensors (Basel, Switzerland) · 2022Pooled it
- The role of face regions in remote photoplethysmography for contactless heart rate monitoring.NPJ digital medicine · 2025Article
- Robust in-vehicle heartbeat detection using multimodal signal fusion.Scientific reports · 2023Article
- Robust Pulse Rate Measurements from Facial Videos in Diverse Environments.Sensors (Basel, Switzerland) · 2022Article
- Heart rate prediction from facial video with masks using eye location and corrected by convolutional neural networks.Biomedical signal processing and control · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Research shows that physiological signals can provide objective data support for the analysis of human emotions. At present, non-contact heart rate data have been employed in the research of medicine, intelligent transportation, smart education, etc. However, it is hard to detect heart rate data using non-contact traditional methods during head rotation, especially when face information is missing in scenarios such as online teaching/learning. Traditional remote photoplethysmography (rPPG) methods require a static, full frontal face within a fixed distance for heart rate detection. These strict requirements make it impractical to measure heart rate data in real-world scenarios, as a lot of videos only partially record the subjects' face information, such as profile, too small distance, and wearing a mask. The current algorithm aims to solve the problem of head deflections between 30 degrees and 45 degrees by employing a symmetry substitution method, which can replace the undetected region of interest (ROI) with the detectable one. When face information is partially missing, our algorithm uses face-eye location to determine ROI. The results show that the method in this paper can solve certain practical problems related to heart rate detection, with a root mean square error (RMSE) under 7.64 bpm.
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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.