Results 61 to 70 of about 828,391 (140)

A comprehensive review of heart rate measurement using remote photoplethysmography and deep learning

BioMedical Engineering OnLine
With the widespread availability of consumer-grade cameras, interest in heart rate (HR) measurement using remote photoplethysmography (rPPG) has grown significantly.
Uday Debnath, Sungho Kim
doaj   +1 more source

Contactless Health Monitoring: An Overview of Video‐Based Techniques Utilising Machine/Deep Learning

IET Wireless Sensor Systems, Volume 15, Issue 1, January/December 2025.
This review article presents a comprehensive examination of contactless health monitoring techniques utilising machine learning (ML) and deep learning (DL) algorithms for the assessment of critical vital signs. ABSTRACT Vital signs are crucial indicators of an individual's physiological well‐being and represent one of the primary evaluations conducted ...
Alaa Hajr, Bahram Tarvirdizadeh, Khalil Alipour, Mohammad Ghamari   +3 more
wiley   +1 more source

A Wearable Bracelet for Simultaneous Monitoring of Transcutaneous Carbon Dioxide and Pulse Rates

Advanced Electronic Materials, Volume 10, Issue 5, May 2024.
This study introduces a wearable bracelet that simultaneously detects transcutaneous carbon dioxide and pulse rate, which serves as crucial biochemical and biophysical signals for chronic disease patients. The wearable bracelet exhibits exceptional sensitivity, selectivity, and anti‐humidity interference.
Yaoxuan Cui, Qingpeng Cao, Xubin Zheng, Hao Dong, Jin Wang, Feng Xu, Libin Qian, Di Wang   +7 more
wiley   +1 more source

Influences on rPPG-Based Spatial Blood Perfusion Maps

Current Directions in Biomedical Engineering
Recent studies show the feasibility of using local remote photoplethysmography (rPPG) for non-contact blood perfusion assessment by creating spatial pulsatile blood perfusion maps. While global rPPG has been widely studied for its robustness, e.g.
Kobel Svenja Nicola, Wuerich Carolin, Ernst Anna Lotta, Fusshoeller Eva, Grueter Jan Niclas, Haendler Jakob, Wiede Christian, Seidl Karsten   +7 more
doaj   +1 more source

Facial and Neck Region Analysis for Deepfake Detection Using Remote Photoplethysmography Signal Similarity

IET Biometrics, Volume 2024, Issue 1, 2024.
Deepfake (DF) involves utilizing artificial intelligence (AI) technology to synthesize or manipulate images, voices, and other human or object data. However, recent times have seen a surge in instances of DF technology misuse, raising concerns about cybercrime and the credibility of manipulated information.
Byeong Seon An, Hyeji Lim, Hyeon Ah Seong, Eui Chul Lee, Jun Wan   +4 more
wiley   +1 more source

Heart Rate Estimation Algorithm Integrating Long and Short-Term Temporal Features

Mathematics
Non-contact heart rate monitoring from facial videos utilizing remote photoplethysmography (rPPG) has gained significant traction in remote health monitoring.
Jie Sun, Zhanwang Zhang, Jiaqi Liu, Lijian Zhou, Songtao Hu   +4 more
doaj   +1 more source

Improved Remote Photoplethysmography Using Machine Learning-Based Filter Bank

Applied Sciences
Remote photoplethysmography (rPPG) is a non-contact technology that monitors heart activity by detecting subtle color changes within the facial blood vessels.
Jukyung Lee, Hyosung Joo, Jihwan Woo
doaj   +1 more source

A Comprehensive Review of Deepfake Detection Techniques Utilizing Remote Photoplethysmography

IEEE Access
Deepfake technology enables the creation of highly realistic but fabricated videos, raising significant concerns about the authenticity and reliability of digital media.
Kavya Sree Kammari, Manju Khanna, Yasasree Lasya Annambhotla, Keerthi Devarajan Anuradha, Poosarla Naga Suryapadma Namrata, K. V. Nagaraja, T. V. Smitha, Tony Jan, Ganesh R. Naik   +8 more
doaj   +1 more source

Low-Complexity Timing Correction Methods for Heart Rate Estimation Using Remote Photoplethysmography

Sensors
With the rise of modern healthcare monitoring, heart rate (HR) estimation using remote photoplethysmography (rPPG) has gained attention for its non-contact, continuous tracking capabilities.
Chun-Chi Chen, Song-Xian Lin, Hyundoo Jeong   +2 more
doaj   +1 more source

A Comparison of Convolutional Neural Network Transfer Learning Regression Models for Remote Photoplethysmography Signal Estimation

AI
This study explores the extraction of remote Photoplethysmography (rPPG) signals from images using various neural network architectures, addressing the challenge of accurate signal estimation in biomedical contexts.
Jana Sturekova, Patrik Kamencay, Peter Sykora, Roberta Hlavata   +3 more
doaj   +1 more source