Non-Contact HR Monitoring via Smartphone and Webcam During Different Respiratory Maneuvers and Body Movements

Contactless Vital Signs Monitoring From Videos Recorded With Digital Cameras: An Overview

“…As in the case of f R estimation, different cameras can be used (Poh et al, 2011;Mestha et al, 2014;Han et al, 2015;Zhang et al, 2017;Maji et al, 2020;Yang et al, 2021a) as well as different user-camera distances (Scalise et al, 2012;Sanyal and Nundy, 2018;Song et al, 2020). The most common distance is about 1 m that allows recording the facial region with the frontal camera of laptop or tablet in several settings (Rasche et al, 2016;Hassan et al, 2017b;Shoushan et al, 2021). Taking into account that the possible HR values ranges from 40 [bradycardia in young users (Kawachi et al, 1995;Longin et al, 2005;Sharma et al, 2015)] to around 220 bpm (which is the maximum HR value in young users or in newborns), a minimum sampling rate of 8 fps should be guaranteed (sampling theorem).…”

“…The ROI used to extract the HR and other related cardiac parameters is typical the facial region. In more detail, different areas can be used as ROI, like forehead, left cheek, right cheek, or the whole face (Favilla et al, 2019;Lamba and Virmani, 2020;Shoushan et al, 2021). Moreover, other studies investigated alternative regions of the skin exposed to light like the palm hand (Cennini et al, 2010;Sun et al, 2012;Sereevoravitgul and Kondo, 2014;Lu et al, 2018;van der Kooij and Naber, 2019).…”

“…Most studies investigate the capabilities of camera-based technologies for monitoring cardiac activity and estimating HR in indoor environments to simulate different scenarios in which these technologies could be applied (Balakrishnan et al, 2013;Tasli et al, 2014;Lamba and Virmani, 2020;Maji et al, 2020;Song et al, 2020;Shoushan et al, 2021). In few studies, experiments to simulate training sessions (e.g., running and cycling) have been performed (Sun, 2011;Tasli et al, 2014), and other studies investigate the potentiality of applying non-contact technologies in the automotive field (Zhang et al, 2014;Huan Qi, 2015;Kuo et al, 2015).…”

Spatial averaging method based on adaptive weight for imaging photoplethysmography

“… – 29 The comparative analysis of the pulse signals obtained from the forearm (dorsal), forearm (ventral), forehead, palm, hand (dorsal), cheek, nose, and whole face was performed, and the results showed that the forehead and cheek can provide the high-quality pulse signal 18 – 20 On the other hand, some authors use facial skin region as ROI 21 – 27 or adaptively select ROI providing high-quality pulse signal 28 . The disadvantage of the above studies 21 – 28 is that they use a specific region of the face as the ROI and obtain the pulse signal from it, so it cannot effectively utilize the pulse information of different parts in the face.…”

Multi-ROI Spectral Approach for the Continuous Remote Cardio-Respiratory Monitoring from Mobile Device Built-In Cameras

“…Among these factors, user–camera distance, the lighting source, and the selected ROI can affect the quality of the r-PPG waveform and thus the accuracy in the estimation of HR [ 9 , 11 , 16 , 17 ]. The ROI selection for HR estimation is a debated issue, single or multiple ROIs in the facial region [ 18 , 19 ] or the palm hand [ 20 ] can be employed, but performances are typically dependent on the specific application and recording environment [ 18 , 19 ]. Conversely, motion-based methods rely on the detection of small amplitude movements recorded by a video camera, such as chest wall movements due to the breathing activity or mechanical movement of the head caused by the flow of the blood from the heart to the head (called remote ballistocardiography—r-BCG) [ 21 , 22 ].…”