ReviewSensors (Basel, Switzerland)2021
Deep Learning Methods for Remote Heart Rate Measurement: A Review and Future Research Agenda.
Review in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.
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
21 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Non-Contact Vision-Based Techniques of Vital Sign Monitoring: Systematic Review.Sensors (Basel, Switzerland) · 2024Pooled it
- Contactless Technologies, Sensors, and Systems for Cardiac and Respiratory Measurement during Sleep: A Systematic Review.Sensors (Basel, Switzerland) · 2023Pooled it
- Stress Management Intervention for Leaders Increases Nighttime SDANN: Results from a Randomized Controlled Trial.International journal of environmental research and public health · 2022Trial
- Remote Photoplethysmography Using Triple-Head Spatio-Temporal Transformer with Reaction-Driven Gating and Illumination Separation.Sensors (Basel, Switzerland) · 2026Article
- Reliability of contactless vital sign measurement algorithms for use in drone-based mass casualty triage.Scientific reports · 2026Article
- Mental Stress Detection Using Physiological Sensors and Artificial Intelligence: A Review.Sensors (Basel, Switzerland) · 2026Review
- Balancing cardiac privacy with quality in video recordings.Communications medicine · 2025Article
- Flexible Wearable Heart Rate Monitoring System and Low-Power Design: A Review.Sensors (Basel, Switzerland) · 2025Review
- Channel attention pyramid network for remote physiological measurement.Scientific reports · 2025Article
- Comparison of Machine Learning Algorithms for Heartbeat Detection Based on Accelerometric Signals Produced by a Smart Bed.Sensors (Basel, Switzerland) · 2024Article
- Contactless Blood Oxygen Saturation Estimation from Facial Videos Using Deep Learning.Bioengineering (Basel, Switzerland) · 2024Article
- Robust in-vehicle heartbeat detection using multimodal signal fusion.Scientific reports · 2023Article
- An Evaluation of Non-Contact Photoplethysmography-Based Methods for Remote Respiratory Rate Estimation.Sensors (Basel, Switzerland) · 2023Article
- Machine learning-based classification analysis of knowledge worker mental stress.Frontiers in public health · 2023Article
- Towards a Machine Learning-Based Digital Twin for Non-Invasive Human Bio-Signal Fusion.Sensors (Basel, Switzerland) · 2022Article
- Blood Pressure Measurement: From Cuff-Based to Contactless Monitoring.Healthcare (Basel, Switzerland) · 2022Review
- Non-Invasive Data Acquisition and IoT Solution for Human Vital Signs Monitoring: Applications, Limitations and Future Prospects.Sensors (Basel, Switzerland) · 2022Review
- Heart rate prediction from facial video with masks using eye location and corrected by convolutional neural networks.Biomedical signal processing and control · 2022Article
- An Embedded System Using Convolutional Neural Network Model for Online and Real-Time ECG Signal Classification and Prediction.Diagnostics (Basel, Switzerland) · 2022Article
- Diagnostic Features and Potential Applications of PPG Signal in Healthcare: A Systematic Review.Healthcare (Basel, Switzerland) · 2022Review
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
No grant is acknowledged in the PubMed record.
Abstract
Heart rate (HR) is one of the essential vital signs used to indicate the physiological health of the human body. While traditional HR monitors usually require contact with skin, remote photoplethysmography (rPPG) enables contactless HR monitoring by capturing subtle light changes of skin through a video camera. Given the vast potential of this technology in the future of digital healthcare, remote monitoring of physiological signals has gained significant traction in the research community. In recent years, the success of deep learning (DL) methods for image and video analysis has inspired researchers to apply such techniques to various parts of the remote physiological signal extraction pipeline. In this paper, we discuss several recent advances of DL-based methods specifically for remote HR measurement, categorizing them based on model architecture and application. We further detail relevant real-world applications of remote physiological monitoring and summarize various common resources used to accelerate related research progress. Lastly, we analyze the implications of research findings and discuss research gaps to guide future explorations.
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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.