ReviewFrontiers in bioengineering and biotechnology2024
Deep learning and remote photoplethysmography powered advancements in contactless physiological measurement.
Review in Frontiers in bioengineering and biotechnology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Perception and accuracy of an AI-based contactless wellness screening system in dental setting; a cross-sectional study.The Saudi dental journal · 2026Article
- Rationale and design of the AutoHealth study: prospective clinical validation of continuous multimodal in-vehicle health monitoring in real-world mobility.European heart journal. Digital health · 2026Article
- Roadmap of remote photoplethysmography from heart rate measurement toward clinical translation.NPJ digital medicine · 2026Article
- Atrial Fibrillation Detection from At-Rest PPG Signals Using an SDOF-TF Method.Sensors (Basel, Switzerland) · 2026Article
- Interventional study of the iSelfie heart health tool for assessment of vital signs (heart rate, blood pressure, and Oxygen Saturation) in the Kingdom of Saudi Arabia.Saudi medical journal · 2025Article
- Motion Artifacts Removal from Measured Arterial Pulse Signals at Rest: A Generalized SDOF-Model-Based Time-Frequency Method.Sensors (Basel, Switzerland) · 2025Article
- Remote Vital Sensing in Clinical Veterinary Medicine: A Comprehensive Review of Recent Advances, Accomplishments, Challenges, and Future Perspectives.Animals : an open access journal from MDPI · 2025Review
- Anomaly detection in medical via multimodal foundation models.Frontiers in bioengineering and biotechnology · 2025Article
- State-dependent facial pulsation asymmetry and phase asynchrony measured by imaging photoplethysmography and their coupling with contingent negative variation in migraine.Frontiers in neurologyArticle
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In recent decades, there has been ongoing development in the application of computer vision (CV) in the medical field. As conventional contact-based physiological measurement techniques often restrict a patient's mobility in the clinical environment, the ability to achieve continuous, comfortable and convenient monitoring is thus a topic of interest to researchers. One type of CV application is remote imaging photoplethysmography (rPPG), which can predict vital signs using a video or image. While contactless physiological measurement techniques have an excellent application prospect, the lack of uniformity or standardization of contactless vital monitoring methods limits their application in remote healthcare/telehealth settings. Several methods have been developed to improve this limitation and solve the heterogeneity of video signals caused by movement, lighting, and equipment. The fundamental algorithms include traditional algorithms with optimization and developing deep learning (DL) algorithms. This article aims to provide an in-depth review of current Artificial Intelligence (AI) methods using CV and DL in contactless physiological measurement and a comprehensive summary of the latest development of contactless measurement techniques for skin perfusion, respiratory rate, blood oxygen saturation, heart rate, heart rate variability, and blood pressure.
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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.