Color Signal Processing Methods for Webcam-Based Heart Rate Evaluation

Kopeliovich, Mikhail; Petrushan, Mikhail

doi:10.1007/978-3-030-29513-4_53

Cited by 2 publications

(2 citation statements)

References 26 publications

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“…The distance of the body region of interest from the camera will pack the pixel density and the additional contextual information that might be available from the image. Helpfully, some datasets have characterized the face ROI pixel density [63], we recommend that future datasets do similarly. Placing a camera very close to the body might lead to a higher number of pixels containing the signal of interest, but also potentially mean that information about other related signals, or context (e.g., body motions/activities) is lost.…”

Section: Imaging Settingsmentioning

confidence: 99%

“…rPPG [63] 16 The rPPG dataset includes 52 recording from three RGB cameras: a Logitech C920 webcam at resolution 1920×1080 (WMV2 video codec), a Microsoft VX800 webcam at resolution 640 × 480 (WMV3 video codec), and a Lenovo B590 laptop integrated webcam at resolution 640× 480 pixels (WMV3 video codec). All recordings were 24-bit depth (3x 8-bit per channel) at 15 Hz.…”

Section: Public Datasetsmentioning

confidence: 99%

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Camera Measurement of Physiological Vital Signs

McDuff¹

2021

Preprint

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The need for remote tools for healthcare monitoring has never been more apparent. Camera measurement of vital signs leverages imaging devices to compute physiological changes by analyzing images of the human body. Building on advances in optics, machine learning, computer vision and medicine these techniques have progressed significantly since the invention of digital cameras. This paper presents a comprehensive survey of camera measurement of physiological vital signs, describing they vital signs that can be measured and the computational techniques for doing so. I cover both clinical and non-clinical applications and the challenges that need to be overcome for these applications to advance from proofs-of-concept. Finally, I describe the current resources (datasets and code) available to the research community and provide a comprehensive webpage (https://cameravitals.github.io/) with links to these resource and a categorized list of all the papers referenced in this article. CCS Concepts: • Human-centered computing → Ubiquitous and mobile computing; • Applied computing → Bioinformatics; • Computing methodologies → Computer vision.

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Section: Imaging Settingsmentioning

confidence: 99%

Section: Public Datasetsmentioning

confidence: 99%