Results 31 to 40 of about 1,600 (150)

DiffPhys: Enhancing Signal-to-Noise Ratio in Remote Photoplethysmography Signal Using a Diffusion Model Approach

Bioengineering
Remote photoplethysmography (rPPG) is an emerging non-contact method for monitoring cardiovascular health based on facial videos. The quality of the captured videos largely determines the efficacy of rPPG in this application. Traditional rPPG techniques,
Shutao Chen, Kwan-Long Wong, Jing-Wei Chin, Tsz-Tai Chan, Richard H. Y. So   +4 more
doaj   +1 more source

RS+rPPG: Robust Strongly Self-Supervised Learning for rPPG [PDF]

Remote photoplethysmography (rPPG) uses RGB facial videos to measure cardiac signals. It holds promise for future applications in telemedicine, affective computing, liveness-based face anti-spoofing, driver monitoring, etc.
Zhao, Guoying, Savic, Marko
core   +1 more source

Heart Rate Measurement Based on 3D Central Difference Convolution with Attention Mechanism

Sensors, 2022
Remote photoplethysmography (rPPG) is a video-based non-contact heart rate measurement technology. It is a fact that most existing rPPG methods fail to deal with the spatiotemporal features of the video, which is significant for the extraction of the ...
Xinhua Liu, Wenqian Wei, Hailan Kuang, Xiaolin Ma   +3 more
doaj   +1 more source

Optimising rPPG Signal Extraction by Exploiting Facial Surface Orientation

, 2022
Remote photoplethysmography (rPPG) is a contactless method to measure human vital signs by detecting subtle skin color changes through a camera. Although many studies have used region of interest (ROI) selection tools to improve rPPG signal extraction ...
Wong, Kwan Long, So, Richard H.Y., Wei Chin, Jing, Chan, Tsz Tai, Tianqu, Kang, Odinaev, Ismoil, Suhartono, Kristian   +6 more
core   +1 more source

Optimizing Remote Photoplethysmography Using Adaptive Skin Segmentation for Real-Time Heart Rate Monitoring

IEEE Access, 2019
Choosing a proper Region of Interest (ROI) for Remote Photoplethysmography (rPPG) is essential and a challenging first step, and it has a direct effect on the accuracy and reliability of the overall heart rate (HR) algorithm.
R. M. Fouad, Osama A. Omer, Moustafa H. Aly   +2 more
doaj   +1 more source

Sykkeen arvioiminen rPPG-menetelmillä haastavissa kuvauksellisissa olosuhteissa

, 2023
The cardiovascular system plays a crucial role in maintaining the body’s equilibrium by regulating blood flow and oxygen supply to different organs and tissues. While contact-based techniques like electrocardiography and photoplethysmography are commonly
Nguyen, Nhi
core  

Non-contact heart rate estimation based on singular spectrum component reconstruction using low-rank matrix and autocorrelation.

PLoS ONE, 2022
The remote photoplethysmography (rPPG) based on cameras, a technology for extracting pulse wave from videos, has been proved to be an effective heart rate (HR) monitoring method and has great potential in many fields; such as health monitoring.
Weibo Wang, Zongkai Wei, Jin Yuan, Yu Fang, Yongkang Zheng   +4 more
doaj   +1 more source

Remote heart rate monitoring - Assessment of the Facereader rPPg by Noldus.

PLoS ONE, 2019
Remote photoplethysmography (rPPG) allows contactless monitoring of human cardiac activity through a video camera. In this study, we assessed the accuracy and precision for heart rate measurements of the only consumer product available on the market ...
Simone Benedetto, Christian Caldato, Darren C Greenwood, Nicola Bartoli, Virginia Pensabene, Paolo Actis   +5 more
doaj   +1 more source

On Using rPPG Signals for DeepFake Detection: A Cautionary Note [PDF]

, 2023
An experimental analysis is proposed concerning the use of physiological signals, specifically remote Photoplethysmography (rPPG), as a potential means for detecting Deepfakes (DF). The study investigates the effects of different variables, such as video
Boccignone G., Grossi G., Cuculo V., Lanzarotti R., D'Amelio A., Valota A., Patania S.   +6 more
core   +1 more source

Advances in vital‐sign prediction and early‐warning models for underground coal mine workers integrating environmental factors

Deep Underground Science and Engineering, EarlyView.
This review synthesizes advances in predicting miners' vital signs by integrating environmental monitoring (dust, temperature, and gas) with physiological data. It highlights multi‐source data fusion techniques and early‐warning models for enhanced occupational safety in underground coal mines.
Junji Zhu, Dawei Cui, Yingjie Liu, Yuntao Liang, Yue Zhang   +4 more
wiley   +1 more source