Approximation-Based Transformation of Color Signal for Heart Rate Estimation with a Webcam

Kopeliovich, Mikhail; Petrushan, Mikhail; Shaposhnikov, D. G.

doi:10.1134/s1054661818040181

Cited by 6 publications

(1 citation statement)

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“…Previous research studies have used a variety of different equipment and set-up to capture data from participants to obtain vital signs. Thermal camera [67], [68], [69], [70], Charge-Coupled Device (CCD) camera [71], [72], other affordable web cameras or those built-in laptops [73], [74], [75], [76], [77], [78], [79], [80], [81], MS Kinect V2 [82], [83], [66], Kinect Azure [84], GoPro camera with a drone [85] and smartphone [86], [87], [88], [89], [90], [91], [92], [93], [94] cameras have been previously used to obtain a person’s vital signs. Table V and Table IV show different studies conducted with various equipment and the vital sign under observation.…”

Section: Rppg Processmentioning

confidence: 99%

Remote Photoplethysmography (rPPG): A State-of-the-Art Review

Pirzada,

Wilde,

Doherty

et al. 2023

Preprint

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Peripheral oxygen saturation (SpO2) and heart rate (HR) are critical physiological measures that clinicians need to observe to decide on an emergency intervention. These measures are typically determined using a contact-based pulse oximeter. This approach may pose difficulties in many cases, such as with young children, patients with burnt or sensitive skin, cognitive impairments, and those undergoing certain medical procedures or severe illnesses. Remote Photoplethysmography (rPPG) allows for unobtrusive sensing of these vital signs in a variety of settings for health monitoring systems. Several research studies have been conducted to use rPPG for this purpose; however, there is still not a commercially available, clinically validated system that overcomes the concerns highlighted in this paper. We present a state-of-the-art review of rPPG-related research conducted including related processes and techniques, such as regions of interest (ROI) selection, extracting the raw signal, pre-processing data, applying noise reduction algorithms, Fast Fourier transforms (FFT), filtering and extracting these vital signs. Further, we present a detailed, critical evaluation of available rPPG systems. Limitations and future directions have also been identified to aid rPPG researchers in further advancing this field.

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