Search Results (69)

Continuous blood pressure (BP) monitoring is essential for the early detection and prevention of cardiovascular diseases like hypertension. Recently, interest in continuous BP estimation systems and algorithms has grown. Various physiological signals reflect BP variations from different perspectives, and combining multiple signals can enhance the accuracy of BP measurements. However, research integrating electrocardiogram (ECG), photoplethysmography (PPG), and impedance cardiography (ICG) signals for BP monitoring remains limited, with related technologies still in early development. A major challenge is the increased system complexity associated with acquiring multiple signals simultaneously, along with the difficulty of efficiently extracting and integrating key features for accurate BP estimation. To address this, we developed a BP monitoring system that can synchronously acquire and process ECG, PPG, and ICG signals. Optimizing the circuit design allowed ECG and ICG modules to share electrodes, reducing components and improving compactness. Using this system, we collected 400 min of signals from 40 healthy subjects, yielding 4390 records. Experiments were conducted to evaluate the system’s performance in BP estimation. The results demonstrated that combining pulse wave analysis features with the XGBoost model yielded the most accurate BP predictions. Specifically, the mean absolute error for systolic blood pressure was 3.76 ± 3.98 mmHg, and for diastolic blood pressure, it was 2.71 ± 2.57 mmHg, both of which achieved grade A performance under the BHS standard. These results are comparable to or better than existing studies based on multi-signal methods. These findings suggest that the proposed system offers an efficient and practical solution for BP monitoring. Full article

Subway train operators face the risk of cumulative cognitive stress due to factors such as visual fatigue from prolonged high-speed tunnel driving, irregular shift patterns, and the monotony of automated operations. This can lead to cognitive decline and human error accidents. Current monitoring of cognitive stress risk predominantly relies on single-modal methods, which are susceptible to environmental interference and offer limited accuracy. This study proposes an intelligent multimodal framework for cognitive stress monitoring by leveraging the symmetry principles in physiological and behavioral manifestations. The symmetry of photoplethysmography (PPG) waveforms and the bilateral symmetry of head movements serve as critical indicators reflecting autonomic nervous system homeostasis and cognitive load. By integrating these symmetry-based features, this study constructs a spatiotemporal dynamic feature set through fusing physiological signals such as PPG and galvanic skin response (GSR) with head and facial behavioral features. Furthermore, leveraging deep learning techniques, a hybrid PSO-CNN-GRU-Attention model is developed. Within this model, the Particle Swarm Optimization (PSO) algorithm dynamically adjusts hyperparameters, and an attention mechanism is introduced to weight multimodal features, enabling precise assessment of cognitive stress states. Experiments were conducted using a full-scale subway driving simulator, collecting data from 50 operators to validate the model’s feasibility. Results demonstrate that the complementary nature of multimodal physiological signals and behavioral features effectively overcomes the limitations of single-modal data, yielding significantly superior model performance. The PSO-CNN-GRU-Attention model achieved a predictive coefficient of determination (R²) of 0.89029 and a mean squared error (MSE) of 0.00461, outperforming the traditional BiLSTM model by approximately 22%. This research provides a high-accuracy, non-invasive solution for detecting cognitive stress in subway operators, offering a scientific basis for occupational health management and the formulation of safe driving intervention strategies. Full article

Recently, technologies monitoring users’ physiological signals in consumer electronics such as smartphones or kiosks with cameras and displays are gaining attention for their potential role in diverse services. While many of these technologies focus on photoplethysmography for the measurement of blood flow changes, respiratory measurement is also essential for assessing an individual’s health status. Previous studies have proposed thermal camera-based and body movement-based respiratory measurement methods. In this paper, we adopt an approach to extract respiratory signals from RGB face videos using photoplethysmography. Prior research shows that photoplethysmography can measure respiratory signals, due to its correlation with cardiac activity, by setting arterial vessel regions as areas of interest for respiratory measurement. However, this correlation does not directly reflect real-time respiratory components in photoplethysmography. Our new approach measures the respiratory rate by capturing changes in skin brightness from motion artifacts. We utilize these brightness factors, including facial movement, for respiratory signal measurement. We applied the wavelet transform and smoothing filters to remove other unrelated motion artifacts. In order to validate our method, we built a dataset of respiratory rate measurements from 20 individuals using an RGB camera in a facial movement-aware environment. Our approach demonstrated a similar performance level to the reference signal obtained with a contact-based respiratory belt, with a correlation above 0.9 and an MAE within 1 bpm. Moreover, our approach offers advantages for real-time measurements, excluding complex computational processes for measuring optical flow caused by the movement of the chest due to respiration. Full article

A photoplethysmography (PPG) pulse in reflection mode represents the change in diffuse reflectance at the skin surface during a cardiac cycle and is commonly used in wearable devices to monitor heart rate. Commercial PPG sensors often rely on the reflectance signal from light sources at two different wavelength regions, green, such as λ = 523 nm, and near infrared (NIR), such as λ = 945 nm. Early in vivo studies of wearable sensors showed that green light is more beneficial than NIR light in optimizing PPG sensitivity. This contradicts the common trends in the standard near infrared spectroscopy techniques, which rely on the long optical pathlengths at NIR wavelengths to achieve optimal depth sensitivity. To quantitatively analyze the spectral characteristics of PPG across the wavelength region of 500–900 nm in a controlled environment, this study performs the spectral measurement of PPG signals using a simple and cost-effective optical phantom model with two distinct layers and a customized diffuse reflectance spectroscopy system. In addition, Monte Carlo simulations are used to elaborate the underlying phenomena at the green and NIR wavelengths when considering different epithelial thicknesses and source–detector distances (SDD). Full article

Work-related musculoskeletal disorders are prevalent in occupations requiring prolonged standing and repetitive movements, often leading to vascular issues and reduced static balance. Innovations in wearable technology, such as smart footwear integrating active systems, aim to mitigate these challenges. This exploratory study assessed the effects of a novel active footwear system, incorporating compression and vibration, on vascular blood flow and static balance in healthy adults. Sixteen healthy participants (seven men and nine women) were randomized into active and placebo phases, each involving repetitive tasks. Outcomes included reflection photoplethysmography, postural sway, and foot volumetry. Data were analyzed pre- and post-intervention, with statistical significance set at p < 0.05. For men, significant improvements in reflection photoplethysmography median values were observed post-active phase (p = 0.031), while women showed no change. Enhanced static balance, reflected in decreased total sway (p = 0.025), was noted in women. No significant changes occurred during the placebo phase. The active system improved vascular function in men and static balance in women, highlighting its potential for ergonomic interventions in industrial settings. Future studies should explore long-term effects and applications in diverse populations, including those with work-related musculoskeletal disorders. Full article

The effect of skin pigmentation on photoplethysmography and, specifically, pulse oximetry has recently received a significant amount of attention amongst researchers, especially since the COVID-19 pandemic. With most computational studies observing overestimation of arterial oxygen saturation (SpO₂) in individuals with darker skin, this study seeks to further investigate the root causes of these discrepancies. This study analysed intensity changes from Monte Carlo-simulated reflectance PPG signals across light, moderate, and dark skin types at oxygen saturations of 70% and 100% in MATLAB R2024a. With simulated intensity reflecting PPG amplitude, the results showed that systolic intensity decreased by 3–4% as pigmentation increased at 660 nm. It was also shown that the impact at 940 nm is minimal (<0.2%), indicating that the increased absorption of red light by melanin has a greater effect on the ratio of ratios calculations. These results suggest that in-built adjustments may be required for data collected from red-light sources in pulse oximeters that do not currently have the necessary post-processing algorithms to account for this difference between diverse skin populations. Full article

A photoplethysmography (PPG) sensor is a cost-effective and efficacious way of measuring health conditions such as heart rate, oxygen saturation, and respiration rate. In this work, we present a hybrid PPG sensor system working in a reflective mode with an optoelectronic module, i.e., the combination of an inorganic light-emitting diode (LED) and a circular-shaped organic photodetector (OPD) surrounding the LED for efficient light harvest followed by the proper driving circuit for accurate PPG signal acquisition. The performance of the hybrid sensor system was confirmed by the heart rate detection process from the PPG using fast Fourier transform analysis. The PPG signal obtained with a 50% LED duty cycle and 250 Hz sampling rate resulted in accurate heart rate monitoring with an acceptable range of error. The effects of the LED duty cycle and the LED luminous intensity were found to be crucial to the heart rate accuracy and to the power consumption, i.e., indispensable factors for the hybrid sensor. Full article

Non-contact heart monitoring is crucial in advancing telemedicine, fitness tracking, and mass screening. Remote photoplethysmography (rPPG) is a non-contact technique to obtain information about heart pulse by analyzing the changes in the light intensity reflected or absorbed by the skin during the blood circulation cycle. However, this technique is sensitive to environmental lightning and different skin pigmentation, resulting in unreliable results. This research presents a multimodal approach to non-contact heart rate estimation by combining facial video and physical attributes, including age, gender, weight, height, and body mass index (BMI). For this purpose, we collected local datasets from 60 individuals containing a 1 min facial video and physical attributes such as age, gender, weight, and height, and we derived the BMI variable from the weight and height. We compare the performance of two machine learning models, support vector regression (SVR) and random forest regression on the multimodal dataset. The experimental results demonstrate that incorporating a multimodal approach enhances model performance, with the random forest model achieving superior results, yielding a mean absolute error (MAE) of 3.057 bpm, a root mean squared error (RMSE) of 10.532 bpm, and a mean absolute percentage error (MAPE) of 4.2% that outperforms the state-of-the-art rPPG methods. These findings highlight the potential for interpretable, non-contact, real-time heart rate measurement systems to contribute effectively to applications in telemedicine and mass screening. Full article

Remote photoplethysmography (rPPG) refers to a non-contact technique that measures heart rate through analyzing the subtle signal changes of facial blood flow captured by video sensors. It is widely used in contactless medical monitoring, remote health management, and activity monitoring, providing a more convenient and non-invasive way to monitor heart health. However, factors such as ambient light variations, facial movements, and differences in light absorption and reflection pose challenges to deep learning-based methods. To solve these difficulties, we put forward a measurement network of heart rate based on multiscale features. In this study, we designed and implemented a dual-branch signal processing framework that combines static and dynamic features, proposing a novel and efficient method for feature fusion, enhancing the robustness and reliability of the signal. Furthermore, we proposed an alternate time-shift module to enhance the model’s temporal depth. To integrate the features extracted at different scales, we utilized a multiscale feature fusion method, enabling the model to accurately capture subtle changes in blood flow. We conducted cross-validation on three public datasets: UBFC-rPPG, PURE, and MMPD. The results demonstrate that MDAR not only ensures fast inference speed but also significantly improves performance. The two main indicators, MAE and MAPE, achieved improvements of at least 30.6% and 30.2%, respectively, surpassing state-of-the-art methods. These conclusions highlight the potential advantages of MDAR for practical applications. Full article

Heart rate (HR) is an important indicator reflecting the overall physical and mental health of the human body, playing a crucial role in diagnosing cardiovascular and neurological diseases. Recent research has revealed that variations in the light absorption of human skin captured through facial video over the cardiac cycle, due to changes in blood volume, can be utilized for non-contact HR estimation. However, most existing methods rely on single-modal video sources (such as RGB or NIR), which often yield suboptimal results due to noise and the limitations of a single information source. To overcome these challenges, this paper proposes a multimodal information fusion architecture named the spatiotemporal sensitive network (SS-Net) for non-contact heart rate estimation. Firstly, spatiotemporal feature maps are utilized to extract physiological signals from RGB and NIR videos effectively. Next, a spatiotemporal sensitive (SS) module is introduced to extract useful physiological signal information from both RGB and NIR spatiotemporal maps. Finally, a multi-level spatiotemporal context fusion (MLSC) module is designed to fuse and complement information between the visible light and infrared modalities. Then, different levels of fused features are refined in task-specific branches to predict both remote photoplethysmography (rPPG) signals and heart rate (HR) signals. Experiments conducted on three datasets demonstrate that the proposed SS-Net achieves superior performance compared to existing methods. Full article

This study evaluated the relationship of non-invasive arterial stiffness parameters with an individual 10-year risk of fatal and non-fatal atherosclerotic cardiovascular disease (ASCVD) events in the cohort post-coronavirus disease 2019 (COVID-19). The study group included 203 convalescents aged 60.0 (55.0–63.0) and 115 (56.7%) women. The ASCVD risk was assessed as low to moderate to very high based on medical history (for 62 participants with pre-existing ASCVD/diabetes/chronic kidney disease in the entire cohort) or calculated in percentages using the Systemic Coronary Risk Evaluation 2 (SCORE2) algorithm based on age, sex, smoking status, systolic blood pressure (BP), and non-high-density lipoprotein cholesterol (for 141 healthy participants). The stiffness index (SI) and reflection index (RI) measured by photoplethysmography, as well as pulse pressure (PP), calculated as the difference between systolic and diastolic BP, were markers of arterial stiffness. Stiffness parameters increased significantly with the increase in ASCVD risk in the entire cohort. In 30 (14.8%) patients in the low- to moderate-risk group, the median SI was 8.07 m/s (7.10–8.73), RI 51.40% (39.40–65.60), and PP 45.50 mmHg (40.00–57.00); in 111 (54.7%) patients in the high-risk group, the median SI was 8.70 m/s (7.40–10.03), RI 57.20% (43.65–68.40), and PP 54.00 mmHg (46.00–60.75); and in 62 (30.5%) patients in the very-high-risk group, the median was SI 9.27 m/s (7.57–10.44), RI 59.00% (50.40–72.40), and PP 60.00 mmHg (51.00–67.00). In healthy participants, the SI ≤ 9.0 m/s (sensitivity of 92.31%, area under the curve [AUC] 0.686, p < 0.001) based on the receiver operating characteristics was the most sensitive variable for discriminating low to moderate risk, and PP > 56.0 mmHg (sensitivity of 74.36%, AUC 0.736, p < 0.001) was used for discriminating very high risk. In multivariate logistic regression, younger age, female sex, PP ≤ 50 mmHg, SI ≤ 9.0 m/s, and triglycerides < 150 mg/dL had the best relationship with low to moderate SCORE2 risk. In turn, older age, currently smoking, PP > 56.0 mmHg, RI > 68.6%, and diastolic BP ≥ 90 mmHg were related to very high SCORE2 risk. In conclusion, arterial stiffness is significantly related to ASCVD risk in post-COVID-19 patients and can be helpful as a single risk marker in everyday practice. Cut-off points for arterial stiffness parameters determined based on SCORE2 may help make individual decisions about implementing lifestyle changes or pharmacological treatment of ASCVD risk factors Full article

Background: Hypertension and atherosclerotic cardiovascular diseases (ASCVD) increase cardiovascular risk and worsen patients’ prognoses. One early predictor of increased risk is a change in arterial stiffness. This study aimed to evaluate arterial stiffness parameters using the non-invasive photoplethysmography (PPG) method in Polish patients with arterial hypertension (AH) and/or atherosclerosis (AS). Methods: The study group consisted of 333 patients (Caucasians, both sexes, aged 30–85 years old). Patients were analyzed in four groups depending on AH and AS (Group I: patients without AH or AS, Group II: AH patients, Group III: AS patients, and Group IV: AH/AS patients) and, in addition, according to sex and history of SARS-CoV-2 infection. Arterial stiffness parameters, i.e., reflection index (RI), peak-to-peak time (PPT), and stiffness index (SI) were automatically calculated with PPG based on the analysis of the pulse wave contour. Results: Mean values of RI and SI were higher in men than women (p < 0.001 each). Diastolic blood pressure (DBP) also differed between sexes (p = 0.010). Mean SI values differed between the study groups (p = 0.038) with the highest SI found in AS/AH patients and the lowest—in patients without AH or AS. The mean SI values were significantly lower in women compared to men in both Group I and Group II (p = 0.006 and p < 0.001, respectively). The mean values of RI were also greater in men than in women in Group I and Group II (p < 0.001 for each group). Regarding COVID-19 history, only HR values differed between patients with and without COVID-19 in AH patients (p = 0.012). In AH patients, men had higher values of RI and SI compared to women (p < 0.001 and p < 0.001). On the other hand, AS women with COVID-19 had significantly greater mean values of SI (9.66 m/s ± 1.61) than men with COVID-19 (7.98 m/s ± 1.09) (p = 0.045). Conclusions: The present study confirmed that sex had a significant impact on arterial stiffness parameters. Both AH and AS affected arterial stiffness. Heart rate was greater in hypertensive patients after COVID-19 compared to hypertensive patients without COVID-19. Full article

The remote monitoring of vital signs via wearable devices holds significant potential for alleviating the strain on hospital resources and elder-care facilities. Among the various techniques available, photoplethysmography stands out as particularly promising for assessing vital signs such as heart rate, respiratory rate, oxygen saturation, and blood pressure. Despite the efficacy of this method, many commercially available wearables, bearing Conformité Européenne marks and the approval of the Food and Drug Administration, are often integrated within proprietary, closed data ecosystems and are very expensive. In an effort to democratize access to affordable wearable devices, our research endeavored to develop an open-source photoplethysmographic sensor utilizing off-the-shelf hardware and open-source software components. The primary aim of this investigation was to ascertain whether the combination of off-the-shelf hardware components and open-source software yielded vital-sign measurements (specifically heart rate and respiratory rate) comparable to those obtained from more expensive, commercially endorsed medical devices. Conducted as a prospective, single-center study, the research involved the assessment of fifteen participants for three minutes in four distinct positions, supine, seated, standing, and walking in place. The sensor consisted of four PulseSensors measuring photoplethysmographic signals with green light in reflection mode. Subsequent signal processing utilized various open-source Python packages. The heart rate assessment involved the comparison of three distinct methodologies, while the respiratory rate analysis entailed the evaluation of fifteen different algorithmic combinations. For one-minute average heart rates’ determination, the Neurokit process pipeline achieved the best results in a seated position with a Spearman’s coefficient of 0.9 and a mean difference of 0.59 BPM. For the respiratory rate, the combined utilization of Neurokit and Charlton algorithms yielded the most favorable outcomes with a Spearman’s coefficient of 0.82 and a mean difference of 1.90 BrPM. This research found that off-the-shelf components are able to produce comparable results for heart and respiratory rates to those of commercial and approved medical wearables. Full article

This study aimed to propose a portable and intelligent rehabilitation evaluation system for digital stroke-patient rehabilitation assessment. Specifically, the study designed and developed a fusion device capable of emitting red, green, and infrared lights simultaneously for photoplethysmography (PPG) acquisition. Leveraging the different penetration depths and tissue reflection characteristics of these light wavelengths, the device can provide richer and more comprehensive physiological information. Furthermore, a Multi-Channel Convolutional Neural Network–Long Short-Term Memory–Attention (MCNN-LSTM-Attention) evaluation model was developed. This model, constructed based on multiple convolutional channels, facilitates the feature extraction and fusion of collected multi-modality data. Additionally, it incorporated an attention mechanism module capable of dynamically adjusting the importance weights of input information, thereby enhancing the accuracy of rehabilitation assessment. To validate the effectiveness of the proposed system, sixteen volunteers were recruited for clinical data collection and validation, comprising eight stroke patients and eight healthy subjects. Experimental results demonstrated the system’s promising performance metrics (accuracy: 0.9125, precision: 0.8980, recall: 0.8970, F1 score: 0.8949, and loss function: 0.1261). This rehabilitation evaluation system holds the potential for stroke diagnosis and identification, laying a solid foundation for wearable-based stroke risk assessment and stroke rehabilitation assistance. Full article

Background: Owing to the association between dysfunctional maternal autonomic regulation and pregnancy complications, assessing non-invasive features reflecting autonomic activity—e.g., heart rate variability (HRV) and the morphology of the photoplethysmography (PPG) pulse wave—may aid in tracking maternal health. However, women with early pregnancy complications typically receive medication, such as corticosteroids, and the effect of corticosteroids on maternal HRV and PPG pulse wave morphology is not well-researched. Methods: We performed a prospective, observational study assessing the effect of betamethasone (a commonly used corticosteroid) on non-invasively assessed features of autonomic regulation. Sixty-one women with an indication for betamethasone were enrolled and wore a wrist-worn PPG device for at least four days, from which five-minute measurements were selected for analysis. A baseline measurement was selected either before betamethasone administration or sufficiently thereafter (i.e., three days after the last injection). Furthermore, measurements were selected 24, 48, and 72 h after betamethasone administration. HRV features in the time domain and frequency domain and describing heart rate (HR) complexity were calculated, along with PPG morphology features. These features were compared between the different days. Results: Maternal HR was significantly higher and HRV features linked to parasympathetic activity were significantly lower 24 h after betamethasone administration. Features linked to sympathetic activity remained stable. Furthermore, based on the PPG morphology features, betamethasone appears to have a vasoconstrictive effect. Conclusions: Our results suggest that administering betamethasone affects maternal autonomic regulation and cardiovasculature. Researchers assessing maternal HRV in complicated pregnancies should schedule measurements before or sufficiently after corticosteroid administration. Full article