Search Results (73)

Remote photoplethysmography (rPPG) enables non-contact vital sign measurements using standard smart device cameras, opening up the potential of scalable health applications on consumer smart devices. However, rPPG signal quality is highly sensitive to camera sensor characteristics and image processing pipelines, which can vary between devices. This variation limits reproducibility and generalisation of rPPG-based algorithms beyond specific hardware platforms. This work presents a reproducible test environment for the validation of the camera sensor response in the context of rPPG signals. A microcontroller-driven illumination system and mechanically constrained setup are used to generate controlled, repeatable optical signals. Two characterisation tests are introduced: a time domain morphology analysis and a frequency domain attenuation analysis. Pulse timing consistency, pulse waveform morphology and normalised frequency responses are compared to assess sensor similarity. This method is applied to selected consumer devices and demonstrates consistent camera response patterns under the controlled test conditions. By explicitly addressing validation of the camera sensor and image processing pipeline, this work supports the development of more robust and transferable rPPG-based vital sign applications across a wider range of consumer devices. Full article

Background. Self-congruency refers to the coherence between emotional experience (internal states) and enacted behavior (outward actions). Reduced self-congruency has been linked to vulnerability in mental health, yet its physiological correlates remain poorly characterized. Heart–brain temporal coupling may provide a candidate physiological marker of this psychological coherence. Methods. Thirty-eight healthy adults underwent resting-state functional magnetic resonance imaging while cardiac activity was simultaneously recorded using photoplethysmography to derive heart rate variability (HRV). Self-congruency was assessed using a graphic rating scale based on the spatial overlap between emotional experience and enacted behavior. Heart–brain temporal coupling between HRV and regional blood-oxygen-level-dependent (BOLD) signals was quantified using cross-covariance analysis across biologically plausible temporal shifts. Results. Heart–brain temporal coupling predominantly reflected brain-to-heart temporal ordering, particularly within regions central to the neurovisceral integration model, including the ventromedial prefrontal and anterior cingulate cortices. In contrast, higher self-congruency was associated with stronger heart-to-brain temporal coupling, notably within the right rostral middle frontal gyrus and supramarginal gyrus, regions implicated in emotion regulation and socio-emotional processing. Conclusions. While global heart–brain temporal coupling is dominated by top-down neural regulation, greater alignment between emotional experience and enacted behavior is associated with enhanced bottom-up cardiac temporal ordering on neural activity. These findings seem to identify a physiological–psychological axis that may inform original prevention-oriented approaches in mental health. Full article

Remote photoplethysmography (rPPG) enables contactless estimation of cardiovascular signals from video, but most existing studies assume a fixed, stationary camera. This study investigates the feasibility of performing real-time moving-average rPPG (MA-rPPG) using a drone-mounted camera, where platform motion, vibration, and viewing distance introduce additional challenges. Building on our previously validated real-time MA-rPPG smart mirror platform, we reuse the smart mirror interface as a unified frontend for visualization, synchronization, and logging while adapting the MA-rPPG pipeline to operate on live video streamed from an off-the-shelf DJI Tello micro-drone. Feasibility experiments were conducted with 10 participants under controlled indoor lighting and constrained flight conditions, where the drone maintained a stable hover in front of a standing subject and facial video was processed in real time to estimate heart rate from a forehead region of interest. To avoid cross-modality bias and clarify the effect of the aerial imaging platform, drone-derived MA-rPPG outputs were compared against a fixed desktop-camera MA-rPPG reference using the same trained model, enabling a controlled, like-for-like evaluation. The results indicate that continuous heart-rate estimation from a drone camera is feasible in our controlled hover-only setup, while agreement tended to vary with hover stability and effective facial resolution. This work is presented strictly as a feasibility-stage investigation and does not claim clinical validity. The findings provide an experimental baseline and operating-envelope insight for future motion-robust rPPG on mobile and aerial health-sensing platforms. Full article

Background and Objectives: Septic shock is marked by profound circulatory and cellular dysfunction, with mortality rates of 25–40% despite guideline-based resuscitation. Normalization of macrohemodynamic variables often fails to restore tissue perfusion, a concept known as hemodynamic incoherence. Persistent microcirculatory dysfunction is associated with organ failure and poor outcomes, underscoring the limitations of systemic monitoring alone. This focused narrative review synthesizes current evidence on microcirculatory monitoring in septic shock, with emphasis on bedside and emerging optical technologies, and evaluates their role as adjuncts to traditional hemodynamic assessment for perfusion-targeted resuscitation. Materials and Methods: A concept-driven search of PubMed/MEDLINE (January 2015 to January 2026) was performed, incorporating MeSH and free-text terms for septic shock, microcirculation, hemodynamic coherence, and monitoring modalities. Foundational pre-2015 studies were included for context. Articles were screened using predefined inclusion/exclusion criteria to minimize bias, with thematic qualitative synthesis. A PRISMA-inspired flow diagram was used to summarize the study selection process. Results: Microcirculatory alterations in septic shock include reduced functional capillary density, perfusion heterogeneity, and impaired oxygen extraction, persisting despite macrohemodynamic correction. Bedside markers, such as capillary refill time (CRT) and mottling, track microvascular recovery more closely than lactate. When used to guide resuscitation, CRT-based strategies show a non-significant mortality trend in randomized evaluation, with later studies reporting benefit in composite clinical outcomes. Optical technologies offer non-invasive insights: photoplethysmography (PPG) and perfusion index (PI) show prognostic value and early detection of incoherence; automated CRT (aCRT) enhances reproducibility; advanced modalities, such as laser speckle contrast imaging (LSCI), near-infrared spectroscopy (NIRS), and sublingual videomicroscopy, provide detailed physiological data but face standardization challenges. Recent interventional evidence, including peripheral perfusion-targeted RCTs, supports improved outcomes, though large-scale trials remain limited. Conclusions: Microcirculatory monitoring provides complementary, physiologically relevant information to macrohemodynamic assessment in septic shock. Emerging bedside tools, such as PI and aCRT, are poised for routine use, while multimodal integration may enable personalized management. Future research should prioritize standardization, AI-driven analysis, and randomized trials to confirm outcome benefits. Full article

This study describes the developed special prototype of a wearable measuring device based on a photoplethysmography (PPG) sensor. It contains also a humidity sensor and a thermometer to measure skin moisture and temperature, and a force-sensitive (FSR) element to sense a contact pressure between the measuring probe and the skin surface. All parts of the multi-sensor are shielded, to be applicable in a weak magnetic field environment. After the basic sensor’s functionality verification inside the magnetic resonance imaging tomograph, a set of experiments was performed. Comparative measurements by an oximeter confirm good correspondence with heart rate values determined from PPG (HR_PPG) and FSR (HR_FSR) signals—the mean absolute error lies below 0.5 min⁻¹ for both types. The sensing of PPG signals on wrists was realized for Normal, Dry, and Wet skin. In comparison with normal skin conditions, drying decreases the PPG signal range by 7% and the systolic pulse width by 8%, while moistening increases the signal ripple by 3% and decreases the correlation between HR_PPG and HR_FSR values by 5%. The detailed analysis per hand and gender types yields differences between male and female subjects, while the results for left and right hands differ less. Full article

Reliable analysis of remote photoplethysmography (rPPG) signals depends on identifying physiologically plausible pulses. Traditional approaches rely on clustering self-similar pulses, which can discard valid variability. Automating pulse quality assessment could capture the true underlying morphology while preserving physiological variability. In this manuscript, individual rPPG pulses were manually labelled as plausible, borderline and implausible and used to train multilayer perceptron classifiers. Two independent datasets were used to ensure strict separation between training and test data: the Vision-MD dataset (4036 facial videos from 1270 participants) and a clinical laboratory dataset (235 videos from 58 participants). Vision-MD data were used for model development with an 80/20 training–validation split and 5-fold cross-validation, while the clinical dataset served exclusively as an independent test set. A three-class model was evaluated achieving $F_1$-scores of 0.92, 0.24 and 0.79 respectively. Recall was highest for plausible and implausible pulses but lower for borderline pulses. To test separability, three pairwise binary classifiers were trained, with ROC-AUC > 0.89 for all three category pairs. When combining borderline and implausible pulses into a single class, the binary classifier achieved an $F_1$-score of 0.93 for the plausible category. Finally, usability analysis showed that automated labelling identified more usable pulses per signal than the previously used agglomerative clustering method, while preserving physiological variability. Full article

Pulsatile signal detection plays an important role in monitoring various physiological parameters, primarily heart rate and blood oxygen saturation. Their applications range from clinical settings to personal health and wellness monitoring. PPG (photoplethysmography) can provide non-invasive optical measurements to detect blood volume changes in peripheral tissues. Yet, it suffers from low spatial resolution to precisely detect the pulsatile signal originating over 2 mm in human tissue. Ultrasound (US) provides a deep detectable range compared to the pure optical method. However, its low contrast to red blood cells and cluster artifacts makes it only detect the indirect pulsation from the surrounding tissue of blood vessels. Recent advances in PA imaging show its capability to precisely measure pulsatile signals originating from blood vessels in deep regions (over 10 mm) and its potential to accurately record blood oxygen saturation with high spatial and temporal resolution. This review article summarizes studies on photoacoustic (PA) pulsatile signal monitoring, highlights the technical advances, and compares it against optical and ultrasonic approaches. Full article

Organic photodetectors (OPDs) offer considerable promise for low-power, solution-processable biosensing and imaging applications; however, their performance remains limited by spectral mismatch and interfacial trap states. In this study, a highly sensitive polymer photodiode was developed via trace incorporation (0.8 wt%) of InP/ZnSe/ZnS quantum dots (QDs) into a PTB7-Th:PC₇₁BM bulk heterojunction (BHJ) matrix. This QD doping approach enhanced the external quantum efficiency (EQE) across the 540–660 nm range and suppressed the dark current density at −2 V by passivating interface trap states. Despite a slight decrease in optical absorption at the optimized composition, the internal quantum efficiency (IQE) increased significantly from ~80% to nearly 95% resulting in a net EQE improvement. This suggests that QD incorporation improved charge transport without compromising charge separation efficiency. As a result, the device achieved a specific detectivity (D*) of 1.8 × 10¹³ Jones, representing a 93% improvement over binary BHJs, along with an ultra-low dark current density of 7.76 × 10⁻¹⁰ A/cm². Excessive QD loading, however, led to optical losses and increased dark current, underscoring the need for precise compositional control. Furthermore, the enhanced detectivity led to a 4 dB improvement in the signal-to-noise ratio (SNR) of photoplethysmography (PPG) signals in the target wavelength range, enabling more reliable biophotonic sensing without increased power consumption. This work demonstrates that QD-based spectral and interfacial engineering offers an effective and scalable route for advancing the performance of OPDs, with broad applicability to low-power biosensors and high-resolution polymer–QD imaging systems. Full article

Photoplethysmography (PPG) is a widely adopted optical technique for cardiovascular monitoring, but its accuracy is often compromised by skin pigmentation, which attenuates the signal in individuals with darker skin tones. This research addresses the challenge of skin pigmentation by developing a PPG sensor system with a novel gain calibration strategy. We present a hardware prototype integrating a programmable gain amplifier (PGA), specifically the OPA3S328 operational amplifier, controlled by a microcontroller. The system performs a one-time gain adjustment at initialization based on the user’s skin tone, which is quantified using RGB image analysis. This “set-and-hold” approach normalizes the signal amplitude across various skin tones while effectively preserving the native morphology of the PPG waveform, which is essential for advanced cardiovascular diagnostics. Experimental validation with over 70 human volunteers demonstrated the PGA’s ability to apply calibrated gain levels, derived from a first-degree polynomial relationship between skin pigmentation and red light absorption. This approach significantly improved signal consistency across different skin tones. The findings highlight the efficacy of pre-measurement gain correction for achieving reliable PPG sensing in diverse populations and lay the groundwork for future optimization of PPG sensor designs to improve reliability in wearable health monitoring devices. Full article

Advancements in camera technology over the past two decades have made image-based monitoring increasingly accessible for healthcare applications. Imaging photoplethysmography (iPPG) and remote photoplethysmography (rPPG) are non-invasive methods for measuring vital signs, such as heart rate, respiratory rate, oxygen saturation, and blood pressure, without physical contact. rPPG utilizes basic cameras to detect physiological changes, while rPPG enables remote monitoring by capturing subtle skin colour variations linked to blood flow. Various rPPG techniques, including colour-based, motion-based, multispectral, and depth-based approaches, enhance accuracy and resilience. These technologies are beneficial not only for healthcare but also for fitness tracking, stress management, and security systems, offering a promising future for contactless physiological monitoring. In this article, there is an overview of these methods and their uniqueness for use in remote photoplethysmography. Full article

Remote photoplethysmography is a technology that estimates heart rate by detecting changes in blood volume induced by heartbeats and the resulting changes in skin color through imaging. This technique is fundamental for the noncontact acquisition of physiological signals from the human body. Despite the notable progress in remote-photoplethysmography algorithms for estimating heart rate from facial videos, challenges remain in accurately assessing heart rate during cardiovascular exercises such as treadmill or elliptical workouts. To address these issues, research has been conducted in various fields. For example, an understanding of optics can help solve these issues. Careful design of video production is also crucial. Approaches in computer vision and deep learning with neural networks can also be applied. We focused on developing a practical approach to improve heart rate estimation algorithms under constrained conditions. To address the limitations of motion blur during high-motion activities, we introduced a novel motion-based algorithm. While existing methods like CHROM, LGI, OMIT, and POS incorporate correction processes, they have shown limited success in environments with significant motion. By analyzing treadmill data, we identified a relationship between motion changes and heart rate. With an initial heart rate provided, our algorithm achieved over a 15 bpm improvement in mean absolute error and root mean squared error compared to existing methods, along with more than double the Pearson correlation. We hope this research contributes to advancements in healthcare and monitoring. Full article

This study explores the extraction of remote Photoplethysmography (rPPG) signals from images using various neural network architectures, addressing the challenge of accurate signal estimation in biomedical contexts. The objective is to evaluate the effectiveness of different models in capturing rPPG signals from dataset snapshots. Two training strategies were investigated: pre-training models with only the fully connected layer being fine-tuned and training the entire network from scratch. The analysis reveals that models trained from scratch consistently outperform their pre-trained counterparts in extracting rPPG signals. Among the architectures assessed, DenseNet121 demonstrated superior performance, offering the most reliable results in this context. These findings underscore the potential of neural networks in advancing rPPG signal extraction, which has promising applications in fields such as clinical monitoring and personalized medical care. This study contributes to the integration of advanced imaging techniques and neural network-based analysis in biomedical engineering, paving the way for more robust and efficient methodologies. Full article

With the rise of modern healthcare monitoring, heart rate (HR) estimation using remote photoplethysmography (rPPG) has gained attention for its non-contact, continuous tracking capabilities. However, most HR estimation methods rely on stable, fixed sampling intervals, while practical image capture often involves irregular frame rates and missing data, leading to inaccuracies in HR measurements. This study addresses these issues by introducing low-complexity timing correction methods, including linear, cubic, and filter interpolation, to improve HR estimation from rPPG signals under conditions of irregular sampling and data loss. Through a comparative analysis, this study offers insights into efficient timing correction techniques for enhancing HR estimation from rPPG, particularly suitable for edge-computing applications where low computational complexity is essential. Cubic interpolation can provide robust performance in reconstructing signals but requires higher computational resources, while linear and filter interpolation offer more efficient solutions. The proposed low-complexity timing correction methods improve the reliability of rPPG-based HR estimation, making it a more robust solution for real-world healthcare applications. Full article

Stress has multiple effects on human health. Sensors designed to measure stress and indicate health status by recognizing illnesses or other conditions (e.g., heart problems and blood pressure) have been widely utilized to monitor and characterize this physiological phenomenon. Stress has two response mechanisms: the autonomic nervous system (ANS) and the hypothalamic–pituitary–adrenal (HPA) axis. The ANS can affect heart rate, breathing rate, skin conductance, blood pressure, and other hemodynamic parameters. Continuous non-invasive blood pressure (cNIBP) measurement, pulse volume, cardiac output, and other hemodynamic parameters are important for stress measurement and health indicators. There is still room for research and the development of different approaches to measurement in this area. Very few sensor systems associated with cNIBP have been developed or are currently in progress. Photoplethysmography (PPG), impedance plethysmography (IPG), and ultrasound imaging were performed along with other non-invasive sensors, such as electrocardiography (ECG), cardioseismography (CSG), and ballistocardiography (BCG), to measure hemodynamic parameters. In the HPA axis, stress hormones are the most important measurement from the perspective of cortisol levels. This measurement is also important in general for the health of the subject, especially for good functioning of the axis itself (HPA axis). Sensors have been developed to detect cortisol levels for academic and research purposes. Cortisol levels can be measured in two ways: direct and indirect hormone measurements. Non-invasive direct hormone measurement uses a sensor to evaluate the cortisol levels in sweat. In contrast, indirect measurement uses the increase or decrease in cortisol levels in relation to other substances such as sodium or potassium. Therefore, in the present study, we investigated technologies, methods, and wearable sensors for continuous hemodynamic measurements at the ANS level and cortisol measurements at the HPA axis level. These sensors and measurements are crucial for improving healthcare applications. Full article

The paper describes a method developed for the indirect cuffless estimation of arterial blood pressure (ABP) from two/three-channel photoplethysmography (PPG) signals. It is important when the actual ABPs cannot be measured, e.g., during scanning inside a magnetic resonance imager. The proposed procedure uses heart pulse transmission parameters (HPTPs) extracted from the second derivative PPG signals. The linear regression method was used to calculate the relation between the determined HPTPs and the ABPs measured in parallel by a blood pressure monitor. The ABP values were estimated by the inverse conversion characteristic calculated from these linear relations. Three auxiliary investigations were performed first to find appropriate settings for PPG signal processing. We tested the accuracy of ABP estimation using two small corpora of multi-channel PPG records sensed during our previous experiments. We also analyzed the distribution of the determined HPTP values depending on the hand and gender for the mapping of a mutual relationship of HPTPs and measured ABPs. The final estimation errors were evaluated graphically (by correlation scatter plots and Bland–Altman plots) and numerically (by a correlation coefficient between the measured and estimated ABPs and by enumeration of the relative estimation error). The obtained results achieve acceptable mean values of −2.6/−3.5 mm Hg for systolic/diastolic ABPs. Full article