Optimal facial areas for webcam-based photoplethysmography

Kopeliovich, Mikhail; Petrushan, Mikhail

doi:10.1134/s1054661816010120

Cited by 6 publications

(4 citation statements)

References 2 publications

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“…Multiple ROIs, forming several input signals (Section (4.1.2) ). Our simple attention model focuses on easy-identifiable parts of the face, known to be important [11,12,14]. CNN expected to use spatial-redundancy as in [23] and extract cross-correlations between signals like in multi-lead ECG [6].…”

Section: Methodsmentioning

confidence: 99%

“…Facial bounding box is detected by the OpenCV implementation of the Viola-Jones face detector [32] applied to each frame. Regarding the works on ROIs selection [11,12], six ROIs are used in this paper: r = 1..6 ( (1), nose bridge (2), areas under eyes (3,4), truncated facial box (5), full bounding box (6). ROIs coordinates are not ideally symmetrical about a horizontal axis: they were set manually to match the facial features on most video sequences in the dataset.…”

Section: Color Signal Extractionmentioning

confidence: 99%

“…Facial bounding box is detected by the OpenCV implementation of the Viola-Jones face detector [32] applied to each frame. Regarding the works on ROIs selection [11,12], six ROIs are used in this paper: r = 1..6 (Fig. 4): corresponding to nose, nose bridge, areas under eyes, truncated facial box, and full bounding box.…”

Section: Color Signal Extractionmentioning

confidence: 99%

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Architectural Tricks for Deep Learning in Remote Photoplethysmography

Kopeliovich

Mironenko

Petrushan

2019

2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)

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Architectural improvements are studied for convolutional network performing estimation of heart rate (HR) values on color signal patches. Color signals are time series of color components averaged over facial regions recorded by webcams in two scenarios: Stationary (without motion of a person) and Mixed Motion (different motion patterns of a person). HR estimation problem is addressed as a classification task, where classes correspond to different heart rate values within the admissible range of [40; 125] bpm. Both adding convolutional filtering layers after fully connected layers and involving combined loss function where first component is a cross entropy and second is a squared error between the network output and smoothed one-hot vector, lead to better performance of HR estimation model in Stationary and Mixed Motion scenarios.

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Section: Methodsmentioning

confidence: 99%

Section: Color Signal Extractionmentioning

confidence: 99%

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Architectural Tricks for Deep Learning in Remote Photoplethysmography

Kopeliovich

Mironenko

Petrushan

2019

2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)

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“…The cardiac rhythm variability was also assessed using the PPGI in the process of monitoring the procedures of electroneurostimulation [ 123 ]. The zones located near the nose and the bridge of the nose are highly informative from the point of view of the possibilities of determining the heart rate when using a webcam [ 124 ].…”

Section: Assessment Of the Human Body Main Physiological Parameters B...mentioning

confidence: 99%

Photoplethysmographic Imaging of Hemodynamics and Two-Dimensional Oximetry

2022

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The review of recent papers devoted to actively developing methods of photoplethysmographic imaging (the PPGI) of blood volume pulsations in vessels and non-contact two-dimensional oximetry on the surface of a human body has been carried out. The physical fundamentals and technical aspects of the PPGI and oximetry have been considered. The manifold of the physiological parameters available for the analysis by the PPGI method has been shown. The prospects of the PPGI technology have been discussed. The possibilities of non-contact determination of blood oxygen saturation SpO 2 (pulse saturation O 2 ) have been described. The relevance of remote determination of the level of oxygenation in connection with the spread of a new coronavirus infection SARS-CoV-2 (COVID-19) has been emphasized. Most of the works under consideration cover the period 2010–2021.

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Color Signal Processing Methods for Webcam-Based Heart Rate Evaluation

Kopeliovich

Petrushan

2019

Advances in Intelligent Systems and Computing

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Optimal facial areas for webcam-based photoplethysmography

Cited by 6 publications

References 2 publications

Architectural Tricks for Deep Learning in Remote Photoplethysmography

Architectural Tricks for Deep Learning in Remote Photoplethysmography

Photoplethysmographic Imaging of Hemodynamics and Two-Dimensional Oximetry

Color Signal Processing Methods for Webcam-Based Heart Rate Evaluation

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