Concentrations of hemoglobin fractions calculation using modified Lambert-Beer law and solving of an ill-posed system of equations

Abdallah, Omar; Natsheh, Mohammed; Alam, Kawther Abo; Qananwah, Qasem; Nabulsi, Ahmed Al; Bolz, A.

doi:10.1117/12.854603

Cited by 4 publications

(4 citation statements)

References 3 publications

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“…4 , the features commonly used by contact PPG can be used by our diffuse reflected light remote-PPG method. Therefore, based on the temporal-only features, commonly used for many contact PPG applications related to hemoglobin concentration changes 20 – 22 , 31 , 42 , 43 and based on the Beer Lambert law, the following features were used, with two extra spatial dimensions yielding the TSTMS features, defined for each pixel location (x, y) in the spatially reduced resolution (50 × 35) face video and at each pulsatile instance t: : Pixel’s gray level value at heart rate signal’s peak for wavelength . : Pixel’s gray level value at heart rate signal’s trough for wavelength .…”

Section: Data Processingmentioning

confidence: 99%

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Classification of emotional states via transdermal cardiovascular spatiotemporal facial patterns using multispectral face videos

Shvimmer

Simhon

Michael

et al. 2022

Sci Rep

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We describe a new method for remote emotional state assessment using multispectral face videos, and present our findings: unique transdermal, cardiovascular and spatiotemporal facial patterns associated with different emotional states. The method does not rely on stereotypical facial expressions but utilizes different wavelength sensitivities (visible spectrum, near-infrared, and long-wave infrared) to gauge correlates of autonomic nervous system activity spatially and temporally distributed across the human face (e.g., blood flow, hemoglobin concentration, and temperature). We conducted an experiment where 110 participants viewed 150 short emotion-eliciting videos and reported their emotional experience, while three cameras recorded facial videos with multiple wavelengths. Spatiotemporal multispectral features from the multispectral videos were used as inputs to a machine learning model that was able to classify participants’ emotional state (i.e., amusement, disgust, fear, sexual arousal, or no emotion) with satisfactory results (average ROC AUC score of 0.75), while providing feature importance analysis that allows the examination of facial occurrences per emotional state. We discuss findings concerning the different spatiotemporal patterns associated with different emotional states as well as the different advantages of the current method over existing approaches to emotion detection.

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Section: Data Processingmentioning

confidence: 99%

“… : Pulsatile amplitude. : Based on a measure of absorption that eliminates the effect of the tissue 20 , 21 , 31 , 43 . : Based on pulsatile amplitudes dissimilarity between two wavelengths, and 20 – 22 .…”

Section: Data Processingmentioning

confidence: 99%

Classification of emotional states via transdermal cardiovascular spatiotemporal facial patterns using multispectral face videos

Shvimmer

Simhon

Michael

et al. 2022

Sci Rep

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“…Drawing upon the research conducted in the domain of contact PPG applications for clinical environments, a comprehensive set of features associated with the pulsatile signal and hemoglobin concentration changes has been developed [ 11 , 12 , 16 , 23 , 35 ]. In the context of the present study, these well-established features have been harnessed and used in a cbPPG to classify personal emotion states as examined in [ 17 ].…”

Section: Methodsmentioning

confidence: 99%

“…These methods measure the changes in blood volume through the amount of light transmitted or reflected after irradiating the skin region [ 13 , 14 ]. The extracted BVP signal also contains information related to cardiovascular activity and heart rate, as well as hemoglobin concentration [ 11 , 12 , 13 , 14 , 15 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

Emotion Classification Based on Pulsatile Images Extracted from Short Facial Videos via Deep Learning

Talala,

Shvimmer,

Simhon

et al. 2024

Sensors

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Most human emotion recognition methods largely depend on classifying stereotypical facial expressions that represent emotions. However, such facial expressions do not necessarily correspond to actual emotional states and may correspond to communicative intentions. In other cases, emotions are hidden, cannot be expressed, or may have lower arousal manifested by less pronounced facial expressions, as may occur during passive video viewing. This study improves an emotion classification approach developed in a previous study, which classifies emotions remotely without relying on stereotypical facial expressions or contact-based methods, using short facial video data. In this approach, we desire to remotely sense transdermal cardiovascular spatiotemporal facial patterns associated with different emotional states and analyze this data via machine learning. In this paper, we propose several improvements, which include a better remote heart rate estimation via a preliminary skin segmentation, improvement of the heartbeat peaks and troughs detection process, and obtaining a better emotion classification accuracy by employing an appropriate deep learning classifier using an RGB camera input only with data. We used the dataset obtained in the previous study, which contains facial videos of 110 participants who passively viewed 150 short videos that elicited the following five emotion types: amusement, disgust, fear, sexual arousal, and no emotion, while three cameras with different wavelength sensitivities (visible spectrum, near-infrared, and longwave infrared) recorded them simultaneously. From the short facial videos, we extracted unique high-resolution spatiotemporal, physiologically affected features and examined them as input features with different deep-learning approaches. An EfficientNet-B0 model type was able to classify participants’ emotional states with an overall average accuracy of 47.36% using a single input spatiotemporal feature map obtained from a regular RGB camera.

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Smartphone-based Human Hemoglobin Level Measurement Analyzing Pixel Intensity of a Fingertip Video on Different Color Spaces

et al. 2018

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Concentrations of hemoglobin fractions calculation using modified Lambert-Beer law and solving of an ill-posed system of equations

Cited by 4 publications

References 3 publications

Classification of emotional states via transdermal cardiovascular spatiotemporal facial patterns using multispectral face videos

Classification of emotional states via transdermal cardiovascular spatiotemporal facial patterns using multispectral face videos

Emotion Classification Based on Pulsatile Images Extracted from Short Facial Videos via Deep Learning

Smartphone-based Human Hemoglobin Level Measurement Analyzing Pixel Intensity of a Fingertip Video on Different Color Spaces

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