An Exploration of Blue PPG Signal Using a Novel Color Sensorbased PPG System

Shchelkanova, Evgeniia; Shchelkanov, Alexander; Shchapova, Liia; Shibata, Tomohiro

doi:10.1109/embc44109.2020.9175745

Cited by 9 publications

(5 citation statements)

References 8 publications

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“…The hemoglobin absorption is highest in the VIS–NIR spectrum, peaking at the blue and green wavelengths. Thus, both channels are expected to have stronger heart rate-related signals due to arterial volume changes, which modulate the diffused reflected light captured by the camera sensor 39 . In addition, the camera’s CMOS sensor is Bayer pattern-based, meaning that there is double the amount of green channel pixels compared to blue and red, yielding lower noise 39 as can be seen in Fig.…”

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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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%

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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show abstract

“…Generally, PPG systems are significantly susceptible to various external interference factors, including individual variations in age, gender, and particularly skin tone, and external factors like motion artifacts, ambient light, applied pressure, and unstable interaction between the sensor and skin. In addition, some physiological characteristics such as local body temperature and perspiration may also involve some disturbances to PPG systems by affecting the blood flow of the skin and the dilated state of blood vessels. ,− In this regard, flexible wearable devices are beneficial to the formation of a stable and adaptive human-device interaction interface, thus reducing interference to the system to a certain extent. Advanced signal processing techniques hold effective functions in minimizing endogenous and exogenous interference and are highly sought-after in wearable sensors and commercial PPG equipment.…”

Section: Optical Methods For Pulse Wave Measurementsmentioning

confidence: 99%

Recent Advances in Materials, Devices and Algorithms Toward Wearable Continuous Blood Pressure Monitoring

Li,

Chu,

Chen

et al. 2024

ACS Nano

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“…The EHR (see Section 3.1.4) was determined from the green channel due to the following several reasons [32,33]: (1) Deoxy-hemoglobin and oxy-hemoglobin show the highest absorption spectra at the blue and green wavelengths followed by the red and NIR wavelength. (2) The signal-to-noise ratio (SNR) for green and blue wavelengths is higher than the red and NIR because of their comparatively shorter path lengths and penetration depths, making them less susceptible to noise from motion (from deeper areas such as bone and fat).…”

Section: Pulsatile Signal Peaks and Troughs Detectionmentioning

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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An Exploration of Blue PPG Signal Using a Novel Color Sensorbased PPG System

Cited by 9 publications

References 8 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

Recent Advances in Materials, Devices and Algorithms Toward Wearable Continuous Blood Pressure Monitoring

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

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