3D Convolutional Neural Networks for Remote Pulse Rate Measurement and Mapping from Facial Video

Overcoming Challenges in Video-Based Health Monitoring: Real-World Implementation, Ethics, and Data Considerations

In the context of evolving healthcare technologies, this study investigates the application of AI and machine learning in video-based health monitoring systems, focusing on the challenges and potential of implementing such systems in real-world scenarios, specifically for knowledge workers. The research underscores the criticality of addressing technological, ethical, and practical hurdles in deploying these systems outside controlled laboratory environments. Methodologically, the study spanned three months and employed advanced facial recognition technology embedded in participants' computing devices to collect physiological metrics such as heart rate, blinking frequency, and emotional states, thereby contributing to a stress detection dataset. This approach ensured data privacy and aligns with ethical standards. The results reveal significant challenges in data collection and processing, including biases in video datasets, the need for high-resolution videos, and the complexities of maintaining data quality and consistency, with 42% (after adjustments) of data lost. In conclusion, this research emphasizes the necessity for rigorous, ethical, and technologically adapted methodologies to fully realize the benefits of these systems in diverse healthcare contexts.

Artificial Neural Networks: A New Frontier in Dental Tissue Regeneration

In the realm of dental tissue regeneration research, various constraints exist such as the potential variance in cell quality, potency arising from differences in donor tissue and tissue microenvironment, the difficulties associated with sustaining long-term and large-scale cell expansion while preserving stemness and therapeutic attributes, as well as the need for extensive investigation into the enduring safety and effectiveness in clinical settings. The adoption of artificial intelligence (AI) technologies has been suggested as a means to tackle these challenges. This is because, tissue regeneration research could be advanced through the use of diagnostic systems that incorporate mining methods such as neural networks (NN), fuzzy, predictive modeling, genetic algorithms, machine learning (ML), cluster analysis, and decision trees. This article seeks to offer foundational insights into a subset of AI referred to as artificial neural networks (ANNs) and assess their potential applications as essential decision-making support tools in the field of dentistry, with a particular focus on tissue engineering research. Although ANNs may initially appear complex and resource intensive, they have proven to be effective in laboratory and therapeutic settings. This expert system can be trained using clinical data alone, enabling their deployment in situations where rule-based decision-making is impractical. As ANNs progress further, it is likely to play a significant role in revolutionizing dental tissue regeneration research, providing promising results in streamlining dental procedures and improving patient outcomes in the clinical setting.

Deep learning and remote photoplethysmography powered advancements in contactless physiological measurement

In recent decades, there has been ongoing development in the application of computer vision (CV) in the medical field. As conventional contact-based physiological measurement techniques often restrict a patient's mobility in the clinical environment, the ability to achieve continuous, comfortable and convenient monitoring is thus a topic of interest to researchers. One type of CV application is remote imaging photoplethysmography (rPPG), which can predict vital signs using a video or image. While contactless physiological measurement techniques have an excellent application prospect, the lack of uniformity or standardization of contactless vital monitoring methods limits their application in remote healthcare/telehealth settings. Several methods have been developed to improve this limitation and solve the heterogeneity of video signals caused by movement, lighting, and equipment. The fundamental algorithms include traditional algorithms with optimization and developing deep learning (DL) algorithms. This article aims to provide an in-depth review of current Artificial Intelligence (AI) methods using CV and DL in contactless physiological measurement and a comprehensive summary of the latest development of contactless measurement techniques for skin perfusion, respiratory rate, blood oxygen saturation, heart rate, heart rate variability, and blood pressure.

cbPPGGAN: A Generic Enhancement Framework for Unpaired Pulse Waveforms in Camera-Based Photoplethysmography

Camera-based photoplethysmography (cbP PG) is a non-contact technique that measures cardiac-related blood volume alterations in skin surface vessels through the analysis of facial videos. While traditional approaches can estimate heart rate (HR) under different illuminations, their accuracy can be affected by motion artifacts, leading to poor waveform fidelity and hindering further analysis of heart rate variability (HRV); deep learning-based approaches reconstruct high-quality pulse waveform, yet their performance significantly degrades under illumination variations. In this work, we aim to leverage the strength of these two methods and propose a framework that possesses favorable generalization capabilities while maintaining waveform fidelity. For this purpose, we propose the cbPPGGAN, an enhancement framework for cbPPG that enables the flexible incorporation of both unpaired and paired data sources in the training process. Based on the waveforms extracted by traditional approaches, the cbPPGGAN reconstructs high-quality waveforms that enable accurate HR estimation and HRV analysis. In addition, to address the lack of paired training data problems in real-world applications, we propose a cycle consistency loss that guarantees the time-frequency consistency before/after mapping. The method enhances the waveform quality of traditional POS approaches in different illumination tests (BH-rPPG) and cross-datasets (UBFC-rPPG) with mean absolute error (MAE) values of 1.34 bpm and 1.65 bpm, and average beat-to-beat (AVBB) values of 27.46 ms and 45.28 ms, respectively. Experimental results demonstrate that the cbPPGGAN enhances cbPPG signal quality and outperforms the state-of-the-art approaches in HR estimation and HRV analysis. The proposed framework opens a new pathway toward accurate HR estimation in an unconstrained environment.

Two-Stage Motion Artifact Reduction Algorithm for rPPG Signals Obtained from Facial Video Recordings

Recent years have witnessed the publication of many research articles regarding the contactless measurement and monitoring of heart rate signals deduced from facial video recordings. The techniques presented in these articles, such as examining the changes in the heart rate of an infant, provide a noninvasive assessment in many cases where the direct placement of any hardware equipment is undesirable. However, performing accurate measurements in cases that include noise motion artifacts still presents an obstacle to overcome. In this research article, a two-stage method for noise reduction in facial video recording is proposed. The first stage of the system consists of dividing each (30) seconds of the acquired signal into (60) partitions and then shifting each partition to the mean level before recombining them to form the estimated heart rate signal. The second stage utilizes the wavelet transform for denoising the signal obtained from the first stage. The denoised signal is compared to a reference signal acquired from a pulse oximeter, resulting in the mean bias error (0.13), root mean square error (3.41) and correlation coefficient (0.97). The proposed algorithm is applied to (33) individuals being subjected to a normal webcam for acquiring their video recording, which can easily be performed at homes, hospitals, or any other environment. Finally, it is worth noting that this noninvasive remote technique is useful for acquiring the heart signal while preserving social distancing, which is a desirable feature in the current period of COVID-19.

X-iPPGNet: A novel one stage deep learning architecture based on depthwise separable convolutions for video-based pulse rate estimation

Pulse rate (PR) is one of the most important markers for assessing a person's health. With the increasing demand for long-term health monitoring, much attention is being paid to contactless PR estimation using imaging photoplethysmography (iPPG). This non-invasive technique is based on the analysis of subtle changes in skin color. Despite efforts to improve iPPG, the existing algorithms are vulnerable to less-constrained scenarios (i.e., head movements, facial expressions, and environmental conditions). In this article, we propose a novel end-to-end spatio-temporal network, namely X-iPPGNet, for instantaneous PR estimation directly from facial video recordings. Unlike most existing systems, our model learns the iPPG concept from scratch without incorporating any prior knowledge or going through the extraction of blood volume pulse signals. Inspired by the Xception network architecture, color channel decoupling is used to learn additional photoplethysmographic information and to effectively reduce the computational cost and memory requirements. Moreover, X-iPPGNet predicts the pulse rate from a short time window (2 s), which has advantages with high and sharply fluctuating pulse rates. The experimental results revealed high performance under all conditions including head motions, facial expressions, and skin tone. Our approach significantly outperforms all current state-of-the-art methods on three benchmark datasets: MMSE-HR (MAE = 4.10 ; RMSE = 5.32 ; r = 0.85), UBFC-rPPG (MAE = 4.99 ; RMSE = 6.26 ; r = 0.67), MAHNOB-HCI (MAE = 3.17 ; RMSE = 3.93 ; r = 0.88).

Non-contact heart rate estimation based on singular spectrum component reconstruction using low-rank matrix and autocorrelation

The remote photoplethysmography (rPPG) based on cameras, a technology for extracting pulse wave from videos, has been proved to be an effective heart rate (HR) monitoring method and has great potential in many fields; such as health monitoring. However, the change of facial color intensity caused by cardiovascular activities is weak. Environmental illumination changes and subjects' facial movements will produce irregular noise in rPPG signals, resulting in distortion of heart rate pulse signals and affecting the accuracy of heart rate measurement. Given the irregular noises such as motion artifacts and illumination changes in rPPG signals, this paper proposed a new method named LA-SSA. It combines low-rank sparse matrix decomposition and autocorrelation function with singular spectrum analysis (SSA). The low-rank sparse matrix decomposition is employed to globally optimize the components of the rPPG signal obtained by SSA, and some irregular noise is removed. Then, the autocorrelation function is used to optimize the global optimization results locally. The periodic components related to the heartbeat signal are selected, and the denoised rPPG signal is obtained by weighted reconstruction with a singular value ratio. The experiment using UBFC-RPPG and PURE database is performed to assess the performance of the method proposed in this paper. The average absolute error was 1.37 bpm, the 95% confidence interval was -7.56 bpm to 6.45 bpm, and the Pearson correlation coefficient was 98%, superior to most existing video-based heart rate extraction methods. Experimental results show that the proposed method can estimate HR effectively.

Heart rate estimation network from facial videos using spatiotemporal feature image

Remote health monitoring has become quite inevitable after SARS-CoV-2 pandemic and continues to be accepted as a measure of healthcare in future too. However, contact-less measurement of vital sign, like Heart Rate(HR) is quite difficult to measure because, the amplitude of physiological signal is very weak and can be easily degraded due to noise. The various sources of noise are head movements, variation in illumination or acquisition devices. In this paper, a video-based noise-less cardiopulmonary measurement is proposed. 3D videos are converted to 2D Spatio-Temporal Images (STI), which suppresses noise while preserving temporal information of Remote Photoplethysmography(rPPG) signal. The proposed model projects a new motion representation to CNN derived using wavelets, which enables estimation of HR under heterogeneous lighting condition and continuous motion. STI is formed by the concatenation of feature vectors obtained after wavelet decomposition of subsequent frames. STI is provided as input to CNN for mapping the corresponding HR values. The proposed approach utilizes the ability of CNN to visualize patterns. Proposed approach yields better results in terms of estimation of HR on four benchmark dataset such as MAHNOB-HCI, MMSE-HR, UBFC-rPPG and VIPL-HR.

Deep learning-based remote-photoplethysmography measurement from short-time facial video

Objective. Efficient non-contact heart rate (HR) measurement from facial video has received much attention in health monitoring. Past methods relied on prior knowledge and an unproven hypothesis to extract remote photoplethysmography (rPPG) signals, e.g. manually designed regions of interest (ROIs) and the skin reflection model.Approach. This paper presents a short-time end to end HR estimation framework based on facial features and temporal relationships of video frames. In the proposed method, a deep 3D multi-scale network with cross-layer residual structure is designed to construct an autoencoder and extract robust rPPG features. Then, a spatial-temporal fusion mechanism is proposed to help the network focus on features related to rPPG signals. Both shallow and fused 3D spatial-temporal features are distilled to suppress redundant information in the complex environment. Finally, a data augmentation strategy is presented to solve the problem of uneven distribution of HR in existing datasets.Main results. The experimental results on four face-rPPG datasets show that our method overperforms the state-of-the-art methods and requires fewer video frames. Compared with the previous best results, the proposed method improves the root mean square error (RMSE) by 5.9%, 3.4% and 21.4% on the OBF dataset (intra-test), COHFACE dataset (intra-test) and UBFC dataset (cross-test), respectively.Significance. Our method achieves good results on diverse datasets (i.e. highly compressed video, low-resolution and illumination variation), demonstrating that our method can extract stable rPPG signals in short time.

Performance analysis of remote photoplethysmography deep filtering using long short-term memory neural network

BACKGROUND

Remote photoplethysmography (rPPG) is a technique developed to estimate heart rate using standard video cameras and ambient light. Due to the multiple sources of noise that deteriorate the quality of the signal, conventional filters such as the bandpass and wavelet-based filters are commonly used. However, after using conventional filters, some alterations remain, but interestingly an experienced eye can easily identify them.

RESULTS

We studied a long short-term memory (LSTM) network in the rPPG filtering task to identify these alterations using many-to-one and many-to-many approaches. We used three public databases in intra-dataset and cross-dataset scenarios, along with different protocols to analyze the performance of the method. We demonstrate how the network can be easily trained with a set of 90 signals totaling around 45 min. On the other hand, we show the stability of the LSTM performance with six state-of-the-art rPPG methods.

CONCLUSIONS

This study demonstrates the superiority of the LSTM-based filter experimentally compared with conventional filters in an intra-dataset scenario. For example, we obtain on the VIPL database an MAE of 3.9 bpm, whereas conventional filtering improves performance on the same dataset from 10.3 bpm to 7.7 bpm. The cross-dataset approach presents a dependence in the network related to the average signal-to-noise ratio on the rPPG signals, where the closest signal-to-noise ratio values in the training and testing set the better. Moreover, it was demonstrated that a relatively small amount of data are sufficient to successfully train the network and outperform the results obtained by classical filters. More precisely, we have shown that about 45 min of rPPG signal could be sufficient to train an effective LSTM deep-filter.

Intelligent Remote Photoplethysmography-Based Methods for Heart Rate Estimation from Face Videos: A Survey

Over the last few years, a rich amount of research has been conducted on remote vital sign monitoring of the human body. Remote photoplethysmography (rPPG) is a camera-based, unobtrusive technology that allows continuous monitoring of changes in vital signs and thereby helps to diagnose and treat diseases earlier in an effective manner. Recent advances in computer vision and its extensive applications have led to rPPG being in high demand. This paper specifically presents a survey on different remote photoplethysmography methods and investigates all facets of heart rate analysis. We explore the investigation of the challenges of the video-based rPPG method and extend it to the recent advancements in the literature. We discuss the gap within the literature and suggestions for future directions.

Availability and performance of face based non-contact methods for heart rate and oxygen saturation estimations: A systematic review

BACKGROUND

Consumer-level cameras have provided an advantage of designing cost-effective, non-contact physiological parameters estimation approaches which is not possible with gold standard estimation techniques. This encourages the development of non-contact estimation methods using camera technology. Therefore, this work aims to present a systematic review summarizing the currently existing face-based non-contact methods along with their performance.

METHODS

This review includes all heart rate (HR) and oxygen saturation (SpO2) studies published in journals and a few reputed conferences, which have compared the proposed estimation methods with one or more standard reference devices. The articles were collected from the following research databases: Institute of Electrical and Electronics Engineers (IEEE), PubMed, Web of Science (WoS), Science Direct, and Association of Computer Machinery (ACM) digital library. All database searches were completed on May 20, 2021. Each study was assessed using a finite set of identified factors for reporting bias.

RESULTS

Out of 332 identified studies, 32 studies were selected for the final review. Additionally, 18 studies were included by thoroughly checking these studies. 3 out of 50 (6%) studies were performed in clinical conditions, while the remaining studies were carried out on a healthy population. 42 out of 50 (84%) studies have estimated HR, while 5/50 (10%) studies have measured SpO₂ only. The remaining three studies have estimated both parameters. The majority of the studies have used 1-3 min videos for estimation. Among the estimation methods, Deep Learning and Independent component analysis (ICA) were used by 11/42 (26.19%) and 9/42 (21.42%) studies, respectively. According to the Bland-Altman analysis, only 8/45 (17.77%) HR studies achieved the clinically accepted error limits whereas, for SpO₂, 4/5 (80%) studies have matched the industry standards (±3%).

DISCUSSION

Deep Learning and ICA have been predominantly used for HR estimations. Among deep learning estimation methods, convolutional neural networks have been employed till date due to their good generalization ability. Most non-contact HR estimation methods need significant improvements to implement these methods in a clinical environment. Furthermore, these methods need to be tested on the subjects suffering from any related disease. SpO₂ estimation studies are challenging and need to be tested by conducting hypoxemic events. The authors would encourage reporting the detailed information about the study population, the use of longer videos, and appropriate performance metrics and testing under abnormal HR and SpO₂ ranges for future estimation studies.

Using High-Fidelity Avatars to Advance Camera-based Cardiac Pulse Measurement

Non-contact physiological measurement has the potential to provide low-cost, non-invasive health monitoring. However, machine vision approaches are often limited by the availability and diversity of annotated video datasets resulting in poor generalization to complex real-life conditions. To address these challenges, this work proposes the use of synthetic avatars that display facial blood flow changes and allow for systematic generation of samples under a wide variety of conditions. Our results show that training on both simulated and real video data can lead to performance gains under challenging conditions. We show strong performance on three large benchmark datasets and improved robustness to skin type and motion. These results highlight the promise of synthetic data for training camera-based pulse measurement; however, further research and validation is needed to establish whether synthetic data alone could be sufficient for training models.

iPPG 2 cPPG: Reconstructing contact from imaging photoplethysmographic signals using U-Net architectures

Imaging photoplethysmography (iPPG) is an optical technique dedicated to the assessment of several vital functions using a simple camera. Significant efforts have been made to reliably estimate heart and respiratory rates. Currently, research is focusing on the remote estimation of oxygen saturation and blood pressure (BP). The limited number of publicly available data tends to restrict the advancements related to BP estimation. To overcome this limit, we propose to split the problem in a two-stage processing chain: (i) converting iPPG to contact PPG (cPPG) signals using available video dataset and (ii) estimate BP from converted cPPG signals by exploiting large existing databases (e.g. MIMIC). This article presents the first developments where a method for converting iPPG signals measured using a camera into cPPG signals measured by contact sensors is proposed. Real and imaginary parts of the continuous wavelet transform (CWT) of cPPG and iPPG signals are passed to various deep pre-trained U-shaped architectures. Conventional metrics and specific waveform estimators have been implemented to validate the relevance of the predictions. The results exhibit good agreements towards a large portion of metrics, showing that the neural architectures properly estimated cPPG from iPPG signals through their CWT representations. The performance indicates that BP estimation from iPPG signals converted to cPPG signals can now be envisaged. Consequently, future work will focus on the integration of models dedicated to BP estimation trained on MIMIC. This is the first demonstration of a method for accurate reconstruction of cPPG from iPPG signals satisfying pulse waveform criteria.

Deep Learning Methods for Remote Heart Rate Measurement: A Review and Future Research Agenda

Heart rate (HR) is one of the essential vital signs used to indicate the physiological health of the human body. While traditional HR monitors usually require contact with skin, remote photoplethysmography (rPPG) enables contactless HR monitoring by capturing subtle light changes of skin through a video camera. Given the vast potential of this technology in the future of digital healthcare, remote monitoring of physiological signals has gained significant traction in the research community. In recent years, the success of deep learning (DL) methods for image and video analysis has inspired researchers to apply such techniques to various parts of the remote physiological signal extraction pipeline. In this paper, we discuss several recent advances of DL-based methods specifically for remote HR measurement, categorizing them based on model architecture and application. We further detail relevant real-world applications of remote physiological monitoring and summarize various common resources used to accelerate related research progress. Lastly, we analyze the implications of research findings and discuss research gaps to guide future explorations.

Non-Contact Heart Rate Estimation via Adaptive RGB/NIR Signal Fusion

We propose a non-contact heart rate (HR) estimation method that is robust to various situations, such as bright, low-light, and varying illumination scenes. We utilize a camera that records red, green, and blue (RGB) and near-infrared (NIR) information to capture the subtle skin color changes induced by the cardiac pulse of a person. The key novelty of our method is the adaptive fusion of RGB and NIR signals for HR estimation based on the analysis of background illumination variations. RGB signals are suitable indicators for HR estimation in bright scenes. Conversely, NIR signals are more reliable than RGB signals in scenes with more complex illumination, as they can be captured independently of the changes in background illumination. By measuring the correlations between the lights reflected from the background and facial regions, we adaptively utilize RGB and NIR observations for HR estimation. The experiments demonstrate the effectiveness of the proposed method.

A Review of Deep Learning-Based Contactless Heart Rate Measurement Methods

The interest in contactless or remote heart rate measurement has been steadily growing in healthcare and sports applications. Contactless methods involve the utilization of a video camera and image processing algorithms. Recently, deep learning methods have been used to improve the performance of conventional contactless methods for heart rate measurement. After providing a review of the related literature, a comparison of the deep learning methods whose codes are publicly available is conducted in this paper. The public domain UBFC dataset is used to compare the performance of these deep learning methods for heart rate measurement. The results obtained show that the deep learning method PhysNet generates the best heart rate measurement outcome among these methods, with a mean absolute error value of 2.57 beats per minute and a mean square error value of 7.56 beats per minute.

PulseGAN: Learning to Generate Realistic Pulse Waveforms in Remote Photoplethysmography

Remote photoplethysmography (rPPG) is a non-contact technique for measuring cardiac signals from facial videos. High-quality rPPG pulse signals are urgently demanded in many fields, such as health monitoring and emotion recognition. However, most of the existing rPPG methods can only be used to get average heart rate (HR) values due to the limitation of inaccurate pulse signals. In this paper, a new framework based on generative adversarial network, called PulseGAN, is introduced to generate realistic rPPG pulse signals through denoising the chrominance (CHROM) signals. Considering that the cardiac signal is quasi-periodic and has apparent time-frequency characteristics, the error losses defined in time and spectrum domains are both employed with the adversarial loss to enforce the model generating accurate pulse waveforms as its reference. The proposed framework is tested on three public databases. The results show that the PulseGAN framework can effectively improve the waveform quality, thereby enhancing the accuracy of HR, the interbeat interval (IBI) and the related heart rate variability (HRV) features. The proposed method significantly improves the quality of waveforms compared to the input CHROM signals, with the mean absolute error of AVNN (the average of all normal-to-normal intervals) reduced by 41.19%, 40.45%, 41.63%, and the mean absolute error of SDNN (the standard deviation of all NN intervals) reduced by 37.53%, 44.29%, 58.41%, in the cross-database test on the UBFC-RPPG, PURE, and MAHNOB-HCI databases, respectively. This framework can be easily integrated with other existing rPPG methods to further improve the quality of waveforms, thereby obtaining more reliable IBI features and extending the application scope of rPPG techniques.

Noncontact Physiological Measurement Using a Camera: A Technical Review and Future Directions

Using a camera as an optical sensor to monitor physiological parameters has garnered considerable research interest in biomedical engineering in recent decades. Researchers have explored the use of a camera for monitoring a variety of physiological waveforms, together with the vital signs carried by these waveforms. Most of the obtained waveforms are related to the human respiratory and cardiovascular systems, and in addition of being indicative of overall health, they can also detect early signs of certain diseases. While using a camera for noncontact physiological signal monitoring offers the advantages of low cost and operational ease, it also has the disadvantages such as vulnerability to motion and lack of burden-free calibration solutions in some use cases. This study presents an overview of the existing camera-based methods that have been reported in recent years. It introduces the physiological principles behind these methods, signal acquisition approaches, various types of acquired signals, data processing algorithms, and application scenarios of these methods. It also discusses the technological gaps between the camera-based methods and traditional medical techniques, which are mostly contact-based. Furthermore, we present the manner in which noncontact physiological signal monitoring use has been extended, particularly over the recent years, to more day-to-day aspects of individuals' lives, so as to go beyond the more conventional use case scenarios. We also report on the development of novel approaches that facilitate easier measurement of less often monitored and recorded physiological signals. These have the potential of ushering a host of new medical and lifestyle applications. We hope this study can provide useful information to the researchers in the noncontact physiological signal measurement community.

Spatio-temporal and -spectral feature maps in photoplethysmography imaging and infrared thermograph

BACKGROUND

Only a small fraction of the information available is generally used in the majority of camera-based sensing approaches for vital sign monitoring. Dedicated skin pixels, for example, fall into this category while other regions are often disregarded early in the processing chain.

METHODS

We look at a simple processing chain for imaging where a video stream is converted to several other streams to investigate whether other image regions should also be considered. These streams are generated by mapping spatio-temporal and -spectral features of video segments and, thus, compressing the information contained in several seconds of video and encoding these in a new image. Two typical scenarios are provided as examples to study the applicability of these maps: face videos in a laboratory setting and measurements of a baby in the neonatal intensive care unit. Each measurement consists of the synchronous recording of photoplethysmography imaging (PPGI) and infrared thermography (IRT). We report the results of a visual inspection of those maps, evaluate the root mean square (RMS) contrast of foreground and background regions, and use histogram intersections as a tool for similarity measurements.

RESULTS

The maps allow us to distinguish visually between pulsatile foreground objects and an image background, which is found to be a noisy pattern. Distortions in the maps could be localized and the origin could be discovered. The IRT highlights subject contours for the heart frequency band, while silhouettes show strong signals in PPGI. Reflections and shadows were found to be sources of signals and distortions. We can testify advantages for the use of near-infrared light for PPGI. Furthermore, a difference in RMS contrast for pulsatile and non-pulsatile regions could be demonstrated. Histogram intersections allowed us to differentiate between the background and foreground.

CONCLUSIONS

We introduced new maps for the two sensing modalities and presented an overview for three different wavelength ranges. The maps can be used as a tool for visualizing aspects of the dynamic information hidden in video streams without automation. We propose focusing on an indirect method to detect pulsatile regions by using the noisy background pattern characteristic, for example, based on the histogram approach introduced.

Biometric Signals Estimation Using Single Photon Camera and Deep Learning

The problem of performing remote biomedical measurements using just a video stream of a subject face is called remote photoplethysmography (rPPG). The aim of this work is to propose a novel method able to perform rPPG using single-photon avalanche diode (SPAD) cameras. These are extremely accurate cameras able to detect even a single photon and are already used in many other applications. Moreover, a novel method that mixes deep learning and traditional signal analysis is proposed in order to extract and study the pulse signal. Experimental results show that this system achieves accurate results in the estimation of biomedical information such as heart rate, respiration rate, and tachogram. Lastly, thanks to the adoption of the deep learning segmentation method and dependability checks, this method could be adopted in non-ideal working conditions—for example, in the presence of partial facial occlusions.