As a kind of physical signals that could be easily acquired in daily life,photoplethysmography(PPG)signal becomes a promising solution to biometric identification for daily access management system(AMS).State-of-the-a...As a kind of physical signals that could be easily acquired in daily life,photoplethysmography(PPG)signal becomes a promising solution to biometric identification for daily access management system(AMS).State-of-the-art PPG-based identification systems are susceptible to the form of motions and physical conditions of the subjects.In this work,to exploit the advantage of deep learning,we developed an improved deep convolutional neural network(CNN)architecture by using the Gram matrix(GM)technique to convert time-serial PPG signals to two-dimensional images with a temporal dependency to improve accuracy under different forms of motions.To ensure a fair evaluation,we have adopted cross-validation method and“training and testing”dataset splitting method on the TROIKA dataset collected in ambulatory conditions.As a result,the proposed GM-CNN method achieved accuracy improvement from 69.5%to 92.4%,which is the best result in terms of multi-class classification compared with state-of-the-art models.Based on average five-fold cross-validation,we achieved an accuracy of 99.2%,improved the accuracy by 3.3%compared with the best existing method for the binary-class.展开更多
In this study,single-channel photoplethysmography(PPG)signals were used to estimate the heart rate(HR),diastolic blood pressure(DBP),and systolic blood pressure(SBP).A deep learning model was proposed using a long-ter...In this study,single-channel photoplethysmography(PPG)signals were used to estimate the heart rate(HR),diastolic blood pressure(DBP),and systolic blood pressure(SBP).A deep learning model was proposed using a long-term recurrent convolutional network(LRCN)modified from a deep learning algorithm,the convolutional neural network model of the modified inception deep learning module,and a long short-term memory network(LSTM)to improve the model’s accuracy of BP and HR measurements.The PPG data of 1,551 patients were obtained from the University of California Irvine Machine Learning Repository.How to design a filter of PPG signals and how to choose the loss functions for deep learning model were also discussed in the study.Finally,the stability of the proposed model was tested using a 10-fold cross-validation,with an MAE±SD of 2.942±5.076 mmHg for SBP,1.747±3.042 mmHg for DBP,and 1.137±2.463 bpm for the HR.Compared with its existing counterparts,the model entailed less computational load and was more accurate in estimating SBP,DBP,and HR.These results established the validity of the model.展开更多
This study proposed a measurement platform for continuous blood pressure estimation based on dual photoplethysmography(PPG)sensors and a deep learning(DL)that can be used for continuous and rapid measurement of blood ...This study proposed a measurement platform for continuous blood pressure estimation based on dual photoplethysmography(PPG)sensors and a deep learning(DL)that can be used for continuous and rapid measurement of blood pressure and analysis of cardiovascular-related indicators.The proposed platform measured the signal changes in PPG and converted them into physiological indicators,such as pulse transit time(PTT),pulse wave velocity(PWV),perfusion index(PI)and heart rate(HR);these indicators were then fed into the DL to calculate blood pressure.The hardware of the experiment comprised 2 PPG components(i.e.,Raspberry Pi 3 Model B and analog-todigital converter[MCP3008]),which were connected using a serial peripheral interface.The DL algorithm converted the stable dual PPG signals acquired from the strictly standardized experimental process into various physiological indicators as input parameters and finally obtained the systolic blood pressure(SBP),diastolic blood pressure(DBP)and mean arterial pressure(MAP).To increase the robustness of the DL model,this study input data of 100 Asian participants into the training database,including those with and without cardiovascular disease,each with a proportion of approximately 50%.The experimental results revealed that the mean absolute error and standard deviation of SBP was 0.17±0.46 mmHg.The mean absolute error and standard deviation of DBP was 0.27±0.52 mmHg.The mean absolute error and standard deviation of MAP was 0.16±0.40 mmHg.展开更多
Owing to the recent trends in remote health monitoring,real-time appli-cations for measuring Heartbeat Rate and Respiration Rate(HARR)from video signals are growing rapidly.Photo Plethysmo Graphy(PPG)is a method that ...Owing to the recent trends in remote health monitoring,real-time appli-cations for measuring Heartbeat Rate and Respiration Rate(HARR)from video signals are growing rapidly.Photo Plethysmo Graphy(PPG)is a method that is operated by estimating the infinitesimal change in color of the human face,rigid motion of facial skin and head parts,etc.Ballisto Cardiography(BCG)is a non-surgical tool for obtaining a graphical depiction of the human body’s heartbeat by inducing repetitive movements found in the heart pulses.The resilience against motion artifacts induced by luminancefluctuation and the patient’s mobility var-iation is the major difficulty faced while processing the real-time video signals.In this research,a video-based HARR measuring framework is proposed based on combined PPG and BCG.Here,the noise from the input video signals is removed by using an Adaptive Kalmanfilter(AKF).Three different algorithms are used for estimating the HARR from the noise-free input signals.Initially,the noise-free sig-nals are subjected to Modified Adaptive Fourier Decomposition(MAFD)and then to Enhanced Hilbert vibration Decomposition(EHVD)andfinally to Improved Var-iation mode Decomposition(IVMD)for attaining three various results of HARR.The obtained values are compared with each other and found that the EHVD is showing better results when compared with all the other methods.展开更多
Heart rate is an important metric for determining physical and mental health.In recent years,remote photoplethysmography(rPPG)has been widely used in characterizing physiological signals in human subjects.Currently,re...Heart rate is an important metric for determining physical and mental health.In recent years,remote photoplethysmography(rPPG)has been widely used in characterizing physiological signals in human subjects.Currently,research on non-contact detection of heart rate mainly focuses on the capture and separation of spectral signals from video imagery.However,this method is very sensitive to the movement of the test subject and light intensity variation,and this results in motion artifacts which presents challenges in extracting accurate physiological signals such as heart rate.In this paper,an improved method for rPPG signal preprocessing is proposed.Based on the well known red green blue(RGB)color space,we segmented skin tone in different color spaces and extracted rPPG signals,after which we use a skin segmentation training model based on the luminance component,the blue-difference chroma components,and red-difference chroma components(YCbCr),as well as hue saturation intensity(HSI)color models.In the experimental verification section,we compare the robustness of the signal on different color spaces.In summary,we are experimentally verifying a better image pre-processing method based on real-time rPPG,which results in more precise measurements through the comparative analysis of skin segmentation and signal quality.展开更多
Continuous and accurate blood pressure(BP)monitoring is critical for personalized hypertension management.However,most existing methods focus on absolute BP estimation,with limited attention to BP changes.To address t...Continuous and accurate blood pressure(BP)monitoring is critical for personalized hypertension management.However,most existing methods focus on absolute BP estimation,with limited attention to BP changes.To address this limitation,we propose a novel framework named Multi-Perspective Differential Feature Space(MDFSBP)for cuffless BP estimation using photoplethysmography(PPG)signals.MDFSBP extracts three perspective differential features:time-based and points-of-interest features,frequency-domain features,and physiological statistical features.Then,an adaptive Multi-Perspective Differential Feature Mapping Module(MDFMM)integrates reconstruction regularization,trend-aware classification,and self-weighted contrastive learning to enhance feature representation and strengthen the association between features and BP changes.Finally,an AutoML-based regression pipeline automates model optimization,improving predictive accuracy and deployment efficiency.To better test the model's capability in capturing BP changes,we introduce a novel abnormality-aware classification metric.We demonstrate BP estimation performance over state-of-the-art(SOTA)methods on both the Mindray and MIMIC datasets.On the Mindray dataset,the model achieves a regression error of 0.17±5.17 mmHg for SBP and 0.05±3.29 mmHg for DBP,with classification accuracy and F1-score reaching 85.25%and 87.50%,respectively.On the MIMIC dataset,it achieves−0.09±5.70 mmHg for SBP and 0.12±4.27 mmHg for DBP,with the classification accuracy and F1-score of 72.84%and 70.66%,respectively.These results highlight the effectiveness,robustness,and generalizability of the proposed frame-work for non-invasive,real-time,and continuous BP monitoring in both clinical and wearable healthcare systems.展开更多
Intraoperative assessment of cerebral hemodynamics is crucial for the success of neurosurgical interventions.This study evaluates the potential of laser speckle contrast imaging(LSCI)and imaging photoplethysmography(I...Intraoperative assessment of cerebral hemodynamics is crucial for the success of neurosurgical interventions.This study evaluates the potential of laser speckle contrast imaging(LSCI)and imaging photoplethysmography(IPPG)for contactless perfusion monitoring during neurosurgery.Despite similarities in their hardware requirements,these techniques rely on fundamentally different principles:light scattering for LSCI and light absorption for IPPG.Comparative experiments were conducted using animals(rats)when assessing the reaction of cerebral hemodynamics to adenosine triphosphate infusion.The results show different spatial and temporal characteristics of the techniques:LSCI predominantly visualizes blood flow in large venous vessels,especially in the sagittal and transverse sinuses,showing a pronounced modulation associated with the heart that cannot be explained by venous blood flow alone.In contrast,IPPG quantifies the dynamics of perfusion changes in the parenchyma,showing minimal signal in large venous vessels.We propose that LSCI signal modulation is significantly influenced by the movement of vessel walls in response to mechanical pressure waves propagating through the parenchyma from nearby arteries.A novel algorithm for LSCI data processing was developed based on this interpretation,producing perfusion indices that align well with IPPG measurements.This study demonstrates that the complementary nature of these techniques(LSCI is sensitive to blood cells displacements,while IPPG detects a change in their density)makes their combined application particularly valuable for comprehensive assessment of cerebral hemodynamics during neurosurgery.展开更多
In its 2023 global health statistics,the World Health Organization noted that noncommunicable diseases(NCDs)remain the leading cause of disease burden worldwide,with cardiovascular diseases(CVDs)resulting in more deat...In its 2023 global health statistics,the World Health Organization noted that noncommunicable diseases(NCDs)remain the leading cause of disease burden worldwide,with cardiovascular diseases(CVDs)resulting in more deaths than the three other major NCDs combined.In this study,we developed a method that can comprehensively detect which CVDs are present in a patient.Specifically,we propose a multi-label classification method that utilizes photoplethysmography(PPG)signals and physiological characteristics from public datasets to classify four types of CVDs and related conditions:hypertension,diabetes,cerebral infarction,and cerebrovascular disease.Our approach to multi-disease classification of cardiovascular diseases(CVDs)using PPG signals achieves the highest classification performance when encompassing the broadest range of disease categories,thereby offering a more comprehensive assessment of human health.We employ a multi-label classification strategy to simultaneously predict the presence or absence of multiple diseases.Specifically,we first apply the Savitzky-Golay(S-G)filter to the PPG signals to reduce noise and then transform into statistical features.We integrate processed PPG signals with individual physiological features as a multimodal input,thereby expanding the learned feature space.Notably,even with a simple machine learning method,this approach can achieve relatively high accuracy.The proposed method achieved a maximum F1-score of 0.91,minimum Hamming loss of 0.04,and an accuracy of 0.95.Thus,our method represents an effective and rapid solution for detecting multiple diseases simultaneously,which is beneficial for comprehensively managing CVDs.展开更多
Diabetes imposes a substantial burden on global healthcare systems.Worldwide,nearly half of individuals with diabetes remain undiagnosed,while conventional diagnostic techniques are often invasive,painful,and expensiv...Diabetes imposes a substantial burden on global healthcare systems.Worldwide,nearly half of individuals with diabetes remain undiagnosed,while conventional diagnostic techniques are often invasive,painful,and expensive.In this study,we propose a noninvasive approach for diabetes detection using photoplethysmography(PPG),which is widely integrated into modern wearable devices.First,we derived velocity plethysmography(VPG)and acceleration plethysmography(APG)signals from PPG to construct multi-channel waveform representations.Second,we introduced a novel multiset spatiotemporal feature fusion framework that integrates hand-crafted temporal,statistical,and nonlinear features with recursive feature elimination and deep feature extraction using a one-dimensional statistical convolutional neural network(1DSCNN).Finally,we developed an interpretable diabetes detection method based on XGBoost,with explainable artificial intelligence(XAI)techniques.Specifically,SHapley Additive exPlanations(SHAP)and Local InterpretableModel-agnostic Explanations(LIME)were employed to identify and interpret potential digital biomarkers associated with diabetes.To validate the proposed method,we extended the publicly available Guilin People’s Hospital dataset by incorporating in-house clinical data from ten subjects,thereby enhancing data diversity.A subject-independent cross-validation strategy was applied to ensure that the testing subjects remained independent of the training data for robust generalization.Compared with existing state-of-the-art methods,our approach achieved superior performance,with an area under the curve(AUC)of 80.5±15.9%,sensitivity of 77.2±7.5%,and specificity of 64.3±18.2%.These results demonstrate that the proposed approach provides a noninvasive,interpretable,and accessible solution for diabetes detection using PPG signals.展开更多
Hemoglobin is a vital protein in red blood cells responsible for transporting oxygen throughout the body.Its accurate measurement is crucial for diagnosing and managing conditions such as anemia and diabetes,where abn...Hemoglobin is a vital protein in red blood cells responsible for transporting oxygen throughout the body.Its accurate measurement is crucial for diagnosing and managing conditions such as anemia and diabetes,where abnormal hemoglobin levels can indicate significant health issues.Traditional methods for hemoglobin measurement are invasive,causing pain,risk of infection,and are less convenient for frequent monitoring.PPG is a transformative technology in wearable healthcare for noninvasive monitoring and widely explored for blood pressure,sleep,blood glucose,and stress analysis.In this work,we propose a hemoglobin estimation method using an adaptive lightweight convolutional neural network(HMALCNN)from PPG.The HMALCNN is designed to capture both fine-grained local waveform characteristics and global contextual patterns,ensuring robust performance across acquisition settings.We validated our approach on two multi-regional datasets containing 152 and 68 subjects,respectively,employing a subjectindependent 5-fold cross-validation strategy.The proposed method achieved root mean square errors(RMSE)of 0.90 and 1.20 g/dL for the two datasets,with strong Pearson correlations of 0.82 and 0.72.We conducted extensive posthoc analyses to assess clinical utility and interpretability.A±1 g/dL clinical error tolerance evaluation revealed that 91.3%and 86.7%of predictions for the two datasets fell within the acceptable clinical range.Hemoglobin range-wise analysis demonstrated consistently high accuracy in the normal and low hemoglobin categories.Statistical significance testing using the Wilcoxon signed-rank test confirmed the stability of performance across validation folds(p>0.05 for both RMSE and correlation).Furthermore,model interpretability was enhanced using Gradient-weighted Class Activation Mapping(Grad-CAM),supporting the model’s clinical trustworthiness.The proposed HMALCNN offers a computationally efficient,clinically interpretable,and generalizable framework for noninvasive hemoglobin monitoring,with strong potential for integration into wearable healthcare systems as a practical alternative to invasive measurement techniques.展开更多
Background: Lower extremity Peripheral artery disease (PAD) is caused by atherosclerosis, or Plaque buildup, that reduces the blood flow to the legs and feet. PAD affects approximately 230 million adults worldwide and...Background: Lower extremity Peripheral artery disease (PAD) is caused by atherosclerosis, or Plaque buildup, that reduces the blood flow to the legs and feet. PAD affects approximately 230 million adults worldwide and is associated with an increased risk of coronary heart disease, stroke, and leg amputation. The first-line method for diagnosis of PAD is the Ankle Brachial Index (ABI), which is the ratio of ankle to brachial higher systolic pressure measured in ankles and arms. The Toe Brachial Index (TBI), which is the ratio of the toe systolic pressure to brachial higher systolic pressure measured in both arms, is considered to be an alternative to the ABI in screening for PAD. The ABI and TBI are measured on the right and left side, and the lower of these numbers is the patient’s overall ABI and TBI. Clinical studies and meta-analysis reviews have shown that the conventional ABI measurement, which uses a cuff, and handheld sphygmomanometer and continuous-wave Doppler tracings, provides an acceptable-to-high specificity level but low sensitivity when compared with vascular color Doppler ultrasound, and/or angiography methods. Another study has shown that the TBI measurement has greater sensitivity but lower specificity than the ABI when compared with vascular color Doppler ultrasound diagnostic based on waveforms. The aim of this clinical study was to evaluate the specificity and sensitivity of the VasoPad System comparing its results to the vascular color doppler ultrasound waveforms. Materials and Methods: The VasoPad System is an automated device using the pulse wave method to measure the arms and ankles dorsalis and tibial posterior artery blood pressures, the photoplethysmography second derivative (PTGSD) to estimate the toe systolic pressure, a patented photoplethysmography (PTG) index marker and volume plethysmography via cuffs during deflation. Vascular Color Doppler ultrasound can diagnose stenosis through the direct visualization of atherosclerosis or plaques and through waveform analysis. The vascular color Doppler ultrasound provides 3 waveform types. The type 1, triphasic waveform is normal blood flow and no atherosclerosis or plaque, the type 2, diphasic waveform is seen when there are atherosclerosis plaques, but normal blood flow, and the type 3, monophasic waveform reflects stenosis with diameter reduction > 50%. Results: The sum of the overall ABI and TBI VasoPad values, called Sum of Brachial Indices (SBI), gave a specificity of 88.89% and sensitivity of 100% for detecting vascular color Doppler ultrasound biphasic and monophasic waveforms versus triphasic waveforms with a cutoff ≤ 1.36 (P Conclusion: The VasoPad was useful for detecting PAD, which is fully defined as having vessel stenosis > 50% (Doppler monophasic waveforms) but also early stage of atherosclerosis plaque of the lower extremities (Doppler biphasic waveforms). The VasoPad method provided a remarkable sensitivity of 100% and a specificity level similar to those of the conventional ABI test method compared with the vascular color Doppler ultrasound. In addition to being useful to screen and detect PAD, the VasoPad offers early detection of lower extremity atherosclerosis, with normal blood flow (Doppler biphasic waveforms), which could provide greater treatment options and thus reduce the overall number of lower extremity complications.展开更多
Deepfake-generated fake faces,commonly utilized in identity-related activities such as political propaganda,celebrity impersonations,evidence forgery,and familiar fraud,pose new societal threats.Although current deepf...Deepfake-generated fake faces,commonly utilized in identity-related activities such as political propaganda,celebrity impersonations,evidence forgery,and familiar fraud,pose new societal threats.Although current deepfake generators strive for high realism in visual effects,they do not replicate biometric signals indicative of cardiac activity.Addressing this gap,many researchers have developed detection methods focusing on biometric characteristics.These methods utilize classification networks to analyze both temporal and spectral domain features of the remote photoplethysmography(rPPG)signal,resulting in high detection accuracy.However,in the spectral analysis,existing approaches often only consider the power spectral density and neglect the amplitude spectrum—both crucial for assessing cardiac activity.We introduce a novel method that extracts rPPG signals from multiple regions of interest through remote photoplethysmography and processes them using Fast Fourier Transform(FFT).The resultant time-frequency domain signal samples are organized into matrices to create Matrix Visualization Heatmaps(MVHM),which are then utilized to train an image classification network.Additionally,we explored various combinations of time-frequency domain representations of rPPG signals and the impact of attention mechanisms.Our experimental results show that our algorithm achieves a remarkable detection accuracy of 99.22%in identifying fake videos,significantly outperforming mainstream algorithms and demonstrating the effectiveness of Fourier Transform and attention mechanisms in detecting fake faces.展开更多
Pulse rate is one of the important characteristics of traditional Chinese medicine pulse diagnosis,and it is of great significance for determining the nature of cold and heat in diseases.The prediction of pulse rate b...Pulse rate is one of the important characteristics of traditional Chinese medicine pulse diagnosis,and it is of great significance for determining the nature of cold and heat in diseases.The prediction of pulse rate based on facial video is an exciting research field for getting palpation information by observation diagnosis.However,most studies focus on optimizing the algorithm based on a small sample of participants without systematically investigating multiple influencing factors.A total of 209 participants and 2,435 facial videos,based on our self-constructed Multi-Scene Sign Dataset and the public datasets,were used to perform a multi-level and multi-factor comprehensive comparison.The effects of different datasets,blood volume pulse signal extraction algorithms,region of interests,time windows,color spaces,pulse rate calculation methods,and video recording scenes were analyzed.Furthermore,we proposed a blood volume pulse signal quality optimization strategy based on the inverse Fourier transform and an improvement strategy for pulse rate estimation based on signal-to-noise ratio threshold sliding.We found that the effects of video estimation of pulse rate in the Multi-Scene Sign Dataset and Pulse Rate Detection Dataset were better than in other datasets.Compared with Fast independent component analysis and Single Channel algorithms,chrominance-based method and plane-orthogonal-to-skin algorithms have a more vital anti-interference ability and higher robustness.The performances of the five-organs fusion area and the full-face area were better than that of single sub-regions,and the fewer motion artifacts and better lighting can improve the precision of pulse rate estimation.展开更多
Diabetes is a widespread and serious disease and noninvasive measurement has been in high demand.To address this problem,a power spectral density-based method was offered for determining glucose sensitive sub-bands in...Diabetes is a widespread and serious disease and noninvasive measurement has been in high demand.To address this problem,a power spectral density-based method was offered for determining glucose sensitive sub-bands in the nearinfrared(NIR)spectrum.The experiments were conducted using phantoms of different optical properties in-vitro conditions.The optical bands 1200–1300 nm and 2100–2200 nm were found feasible for measuring blood glucose.After that,a photoplethysmography(PPG)-based low cost and portable optical system was designed.It has six di®erent NIR wavelength LEDs for illumination and an InGaAs photodiode for detection.Optical density values were calculated through the system and used as independent variables for multiple linear regression analysis.The results of blood glucose levels for 24 known healthy subjects showed that the optical system prediction was nearly 80%in the A zone and 20%in the B zone according to the Clarke Error Grid analysis.It was shown that a promising easyuse,continuous,and compact optical system had been designed.展开更多
Understanding the mechanisms of interaction between bone/bone marrow,circulatory system and nervous system is of great interest due to the potential clinical impact.In humans,the amount of knowledge in this domain rem...Understanding the mechanisms of interaction between bone/bone marrow,circulatory system and nervous system is of great interest due to the potential clinical impact.In humans,the amount of knowledge in this domain remains relatively limited due to the extreme difficulty to monitor these tissues continuously,noninvasively and for long or repeated periods of time.A typical difficult task would be,for example,to continuously monitor bone/bone marrow blood perfusion,hemoglobin oxygen saturation or blood volume and study their dependence on the activity of the autonomic nervous system.In this review article,we want to show that nearinfrared light might be utilized to solve these problems in part.We hope that the present analysis will stimulate future studies in this domain,for which near-infrared light appears as the best available technology today.展开更多
Deception detection plays a crucial role in criminal investigation.Videos contain a wealth of information regarding apparent and physiological changes in individuals,and thus can serve as an effective means of decepti...Deception detection plays a crucial role in criminal investigation.Videos contain a wealth of information regarding apparent and physiological changes in individuals,and thus can serve as an effective means of deception detection.In this paper,we investigate video-based deception detection considering both apparent visual features such as eye gaze,head pose and facial action unit(AU),and non-contact heart rate detected by remote photoplethysmography(rPPG)technique.Multiple wrapper-based feature selection methods combined with the K-nearest neighbor(KNN)and support vector machine(SVM)classifiers are employed to screen the most effective features for deception detection.We evaluate the performance of the proposed method on both a self-collected physiological-assisted visual deception detection(PV3D)dataset and a public bag-oflies(BOL)dataset.Experimental results demonstrate that the SVM classifier with symbiotic organisms search(SOS)feature selection yields the best overall performance,with an area under the curve(AUC)of 83.27%and accuracy(ACC)of 83.33%for PV3D,and an AUC of 71.18%and ACC of 70.33%for BOL.This demonstrates the stability and effectiveness of the proposed method in video-based deception detection tasks.展开更多
Background The use of remote photoplethysmography(rPPG)to estimate blood volume pulse in a noncontact manner has been an active research topic in recent years.Existing methods are primarily based on a singlescale regi...Background The use of remote photoplethysmography(rPPG)to estimate blood volume pulse in a noncontact manner has been an active research topic in recent years.Existing methods are primarily based on a singlescale region of interest(ROI).However,some noise signals that are not easily separated in a single-scale space can be easily separated in a multi-scale space.Also,existing spatiotemporal networks mainly focus on local spatiotemporal information and do not emphasize temporal information,which is crucial in pulse extraction problems,resulting in insufficient spatiotemporal feature modelling.Methods Here,we propose a multi-scale facial video pulse extraction network based on separable spatiotemporal convolution(SSTC)and dimension separable attention(DSAT).First,to solve the problem of a single-scale ROI,we constructed a multi-scale feature space for initial signal separation.Second,SSTC and DSAT were designed for efficient spatiotemporal correlation modeling,which increased the information interaction between the long-span time and space dimensions;this placed more emphasis on temporal features.Results The signal-to-noise ratio(SNR)of the proposed network reached 9.58dB on the PURE dataset and 6.77dB on the UBFC-rPPG dataset,outperforming state-of-the-art algorithms.Conclusions The results showed that fusing multi-scale signals yielded better results than methods based on only single-scale signals.The proposed SSTC and dimension-separable attention mechanism will contribute to more accurate pulse signal extraction.展开更多
Forces acting on the skeleton could be divided into those originating from gravitational loading and those originating from muscle loading. Flat bones in a non-weight-baring segment of the skeleton probably experience...Forces acting on the skeleton could be divided into those originating from gravitational loading and those originating from muscle loading. Flat bones in a non-weight-baring segment of the skeleton probably experience forces mostly generated by muscle contractions. One purpose of muscle contractions is to generate blood flow within skeletal tissues. The present study aimed to investigate the pulsatile patellar bone blood flow after low and high intensity leg extension exercises. Forty-two healthy individuals volunteered for the study. Dynamic isotonic one leg extension/flexion exercises were performed in a leg extension machine. Randomly, the exercises were performed with the left or right leg with either 10 repetition maximum (10 RM) continuously without any resting periods (high intensity muscle work), or 20 RM with a 2 second rest between contractions (low intensity muscle work). The work load, expressed in kilograms totally lifted, was identical in both legs. The pulsatile patellar blood flow was recorded continuously using a photoplethysmographic technique. Blood pressure was measured continuously during muscle work by a non-invasive method (Finapress). The patellar pulsatile bone blood flow increased significantly more after high intensity muscle work (61%) compared to the same work load performed using a lower intensity (22%), p = 0.000073. Systolic blood pressure changed equally during and after both interventions. Post-exercise bone hyperaemia appears to be correlated to the intensity of muscle contractions in the muscle compartment attached to the bone.展开更多
Accurate and portable Blood Pressure(BP)monitoring is vital for managing cardiovascular diseases.However,existing wearable continuous BP monitoring technologies are often inaccurate and rely on external calibration,li...Accurate and portable Blood Pressure(BP)monitoring is vital for managing cardiovascular diseases.However,existing wearable continuous BP monitoring technologies are often inaccurate and rely on external calibration,limiting their practical application in continuous BP monitoring.To address this challenge,we have developed a Wearable continuous non-invasive BP Monitor(WeBPM)equipped with a finger cuff sensor,capable of monitoring BP continuously and accurately within medical-grade precision.WeBPM integrates advanced finger oscillographic BP measurement technology to provide reliable self-calibration functionality.Moreover,Pulsatile Cycle Volume Adjustment Method(PCVAM)we proposed for the closed-loop phase can continuously track changes in vasomotor tone under a controlled frequency based on pulsatile cycles,thereby enabling continuous BP measurement.In comparative experiments with the Nexfin monitor,WeBPM demonstrates excellent performance in induced dynamic BP experiments,with measurement errors of(–1.4±6.24)mmHg for Systolic BP(SBP)and(–0.82±4.83)mmHg for Diastolic BP(DBP).Additionally,compared to clinical invasive reference measurements,WeBPM’s SBP and DBP measurement errors are(–1.74±4.9)mmHg and(0.37±3.28)mmHg,respectively,further proving its outstanding performance.These results highlight WeBPM’s potential in personalized health management and remote monitoring,offering a new solution for continuous non-invasive BP monitoring.展开更多
基金the National Key R&D Program of China(No.2019YFB2204500)the Translational Medicine Cross Research Fund of Shanghai Jiao Tong University(No.ZH2018QNB22)。
文摘As a kind of physical signals that could be easily acquired in daily life,photoplethysmography(PPG)signal becomes a promising solution to biometric identification for daily access management system(AMS).State-of-the-art PPG-based identification systems are susceptible to the form of motions and physical conditions of the subjects.In this work,to exploit the advantage of deep learning,we developed an improved deep convolutional neural network(CNN)architecture by using the Gram matrix(GM)technique to convert time-serial PPG signals to two-dimensional images with a temporal dependency to improve accuracy under different forms of motions.To ensure a fair evaluation,we have adopted cross-validation method and“training and testing”dataset splitting method on the TROIKA dataset collected in ambulatory conditions.As a result,the proposed GM-CNN method achieved accuracy improvement from 69.5%to 92.4%,which is the best result in terms of multi-class classification compared with state-of-the-art models.Based on average five-fold cross-validation,we achieved an accuracy of 99.2%,improved the accuracy by 3.3%compared with the best existing method for the binary-class.
基金This study was supported in part by the Ministry of Science and Technology MOST108-2221-E-150-022-MY3 and Taiwan Ocean University.
文摘In this study,single-channel photoplethysmography(PPG)signals were used to estimate the heart rate(HR),diastolic blood pressure(DBP),and systolic blood pressure(SBP).A deep learning model was proposed using a long-term recurrent convolutional network(LRCN)modified from a deep learning algorithm,the convolutional neural network model of the modified inception deep learning module,and a long short-term memory network(LSTM)to improve the model’s accuracy of BP and HR measurements.The PPG data of 1,551 patients were obtained from the University of California Irvine Machine Learning Repository.How to design a filter of PPG signals and how to choose the loss functions for deep learning model were also discussed in the study.Finally,the stability of the proposed model was tested using a 10-fold cross-validation,with an MAE±SD of 2.942±5.076 mmHg for SBP,1.747±3.042 mmHg for DBP,and 1.137±2.463 bpm for the HR.Compared with its existing counterparts,the model entailed less computational load and was more accurate in estimating SBP,DBP,and HR.These results established the validity of the model.
基金This study was supported in part by the Ministry of Science and Technology MOST 108-2221-E-150-022-MY3 and Taiwan Ocean University.
文摘This study proposed a measurement platform for continuous blood pressure estimation based on dual photoplethysmography(PPG)sensors and a deep learning(DL)that can be used for continuous and rapid measurement of blood pressure and analysis of cardiovascular-related indicators.The proposed platform measured the signal changes in PPG and converted them into physiological indicators,such as pulse transit time(PTT),pulse wave velocity(PWV),perfusion index(PI)and heart rate(HR);these indicators were then fed into the DL to calculate blood pressure.The hardware of the experiment comprised 2 PPG components(i.e.,Raspberry Pi 3 Model B and analog-todigital converter[MCP3008]),which were connected using a serial peripheral interface.The DL algorithm converted the stable dual PPG signals acquired from the strictly standardized experimental process into various physiological indicators as input parameters and finally obtained the systolic blood pressure(SBP),diastolic blood pressure(DBP)and mean arterial pressure(MAP).To increase the robustness of the DL model,this study input data of 100 Asian participants into the training database,including those with and without cardiovascular disease,each with a proportion of approximately 50%.The experimental results revealed that the mean absolute error and standard deviation of SBP was 0.17±0.46 mmHg.The mean absolute error and standard deviation of DBP was 0.27±0.52 mmHg.The mean absolute error and standard deviation of MAP was 0.16±0.40 mmHg.
文摘Owing to the recent trends in remote health monitoring,real-time appli-cations for measuring Heartbeat Rate and Respiration Rate(HARR)from video signals are growing rapidly.Photo Plethysmo Graphy(PPG)is a method that is operated by estimating the infinitesimal change in color of the human face,rigid motion of facial skin and head parts,etc.Ballisto Cardiography(BCG)is a non-surgical tool for obtaining a graphical depiction of the human body’s heartbeat by inducing repetitive movements found in the heart pulses.The resilience against motion artifacts induced by luminancefluctuation and the patient’s mobility var-iation is the major difficulty faced while processing the real-time video signals.In this research,a video-based HARR measuring framework is proposed based on combined PPG and BCG.Here,the noise from the input video signals is removed by using an Adaptive Kalmanfilter(AKF).Three different algorithms are used for estimating the HARR from the noise-free input signals.Initially,the noise-free sig-nals are subjected to Modified Adaptive Fourier Decomposition(MAFD)and then to Enhanced Hilbert vibration Decomposition(EHVD)andfinally to Improved Var-iation mode Decomposition(IVMD)for attaining three various results of HARR.The obtained values are compared with each other and found that the EHVD is showing better results when compared with all the other methods.
基金This work was financially supported by the National Nature Science Foundation of China(Grant Number:61962010).
文摘Heart rate is an important metric for determining physical and mental health.In recent years,remote photoplethysmography(rPPG)has been widely used in characterizing physiological signals in human subjects.Currently,research on non-contact detection of heart rate mainly focuses on the capture and separation of spectral signals from video imagery.However,this method is very sensitive to the movement of the test subject and light intensity variation,and this results in motion artifacts which presents challenges in extracting accurate physiological signals such as heart rate.In this paper,an improved method for rPPG signal preprocessing is proposed.Based on the well known red green blue(RGB)color space,we segmented skin tone in different color spaces and extracted rPPG signals,after which we use a skin segmentation training model based on the luminance component,the blue-difference chroma components,and red-difference chroma components(YCbCr),as well as hue saturation intensity(HSI)color models.In the experimental verification section,we compare the robustness of the signal on different color spaces.In summary,we are experimentally verifying a better image pre-processing method based on real-time rPPG,which results in more precise measurements through the comparative analysis of skin segmentation and signal quality.
基金the Capital Medical Technol-ogy Development Project(2024-IG-4032)National Key Research and Development Program of China(No.2023YFC3603500)+1 种基金the National Natural Science Foundation of China(No.62271023)the Beijing Natural Science Foundation(No.7242269).
文摘Continuous and accurate blood pressure(BP)monitoring is critical for personalized hypertension management.However,most existing methods focus on absolute BP estimation,with limited attention to BP changes.To address this limitation,we propose a novel framework named Multi-Perspective Differential Feature Space(MDFSBP)for cuffless BP estimation using photoplethysmography(PPG)signals.MDFSBP extracts three perspective differential features:time-based and points-of-interest features,frequency-domain features,and physiological statistical features.Then,an adaptive Multi-Perspective Differential Feature Mapping Module(MDFMM)integrates reconstruction regularization,trend-aware classification,and self-weighted contrastive learning to enhance feature representation and strengthen the association between features and BP changes.Finally,an AutoML-based regression pipeline automates model optimization,improving predictive accuracy and deployment efficiency.To better test the model's capability in capturing BP changes,we introduce a novel abnormality-aware classification metric.We demonstrate BP estimation performance over state-of-the-art(SOTA)methods on both the Mindray and MIMIC datasets.On the Mindray dataset,the model achieves a regression error of 0.17±5.17 mmHg for SBP and 0.05±3.29 mmHg for DBP,with classification accuracy and F1-score reaching 85.25%and 87.50%,respectively.On the MIMIC dataset,it achieves−0.09±5.70 mmHg for SBP and 0.12±4.27 mmHg for DBP,with the classification accuracy and F1-score of 72.84%and 70.66%,respectively.These results highlight the effectiveness,robustness,and generalizability of the proposed frame-work for non-invasive,real-time,and continuous BP monitoring in both clinical and wearable healthcare systems.
基金supported by the Russian Science Foundation(Project No.22-65-00096)in terms of experiment planning and carrying out,manufacturing and calibration of the LSCI systemby the Ministry of Science and Higher Education of the Russian Federation(Agreement No.125020301282-0)in terms of manufacturing and calibration of the IPPG system.
文摘Intraoperative assessment of cerebral hemodynamics is crucial for the success of neurosurgical interventions.This study evaluates the potential of laser speckle contrast imaging(LSCI)and imaging photoplethysmography(IPPG)for contactless perfusion monitoring during neurosurgery.Despite similarities in their hardware requirements,these techniques rely on fundamentally different principles:light scattering for LSCI and light absorption for IPPG.Comparative experiments were conducted using animals(rats)when assessing the reaction of cerebral hemodynamics to adenosine triphosphate infusion.The results show different spatial and temporal characteristics of the techniques:LSCI predominantly visualizes blood flow in large venous vessels,especially in the sagittal and transverse sinuses,showing a pronounced modulation associated with the heart that cannot be explained by venous blood flow alone.In contrast,IPPG quantifies the dynamics of perfusion changes in the parenchyma,showing minimal signal in large venous vessels.We propose that LSCI signal modulation is significantly influenced by the movement of vessel walls in response to mechanical pressure waves propagating through the parenchyma from nearby arteries.A novel algorithm for LSCI data processing was developed based on this interpretation,producing perfusion indices that align well with IPPG measurements.This study demonstrates that the complementary nature of these techniques(LSCI is sensitive to blood cells displacements,while IPPG detects a change in their density)makes their combined application particularly valuable for comprehensive assessment of cerebral hemodynamics during neurosurgery.
基金supporting of the National Science and Technology Council NSTC(grant nos.NSTC 112-2221-E-019-023,NSTC 113-2221-E-019-039)Taiwan University of Science and Technology.
文摘In its 2023 global health statistics,the World Health Organization noted that noncommunicable diseases(NCDs)remain the leading cause of disease burden worldwide,with cardiovascular diseases(CVDs)resulting in more deaths than the three other major NCDs combined.In this study,we developed a method that can comprehensively detect which CVDs are present in a patient.Specifically,we propose a multi-label classification method that utilizes photoplethysmography(PPG)signals and physiological characteristics from public datasets to classify four types of CVDs and related conditions:hypertension,diabetes,cerebral infarction,and cerebrovascular disease.Our approach to multi-disease classification of cardiovascular diseases(CVDs)using PPG signals achieves the highest classification performance when encompassing the broadest range of disease categories,thereby offering a more comprehensive assessment of human health.We employ a multi-label classification strategy to simultaneously predict the presence or absence of multiple diseases.Specifically,we first apply the Savitzky-Golay(S-G)filter to the PPG signals to reduce noise and then transform into statistical features.We integrate processed PPG signals with individual physiological features as a multimodal input,thereby expanding the learned feature space.Notably,even with a simple machine learning method,this approach can achieve relatively high accuracy.The proposed method achieved a maximum F1-score of 0.91,minimum Hamming loss of 0.04,and an accuracy of 0.95.Thus,our method represents an effective and rapid solution for detecting multiple diseases simultaneously,which is beneficial for comprehensively managing CVDs.
基金funded by the National Science and Technology Major Project under Grant No.2024ZD0532000 and Grant No.2024ZD0532002the National Natural Science Foundation of China under Grant No.62173318+2 种基金the Shenzhen Basic Research Program under Grant No.JCYJ20250604182831042the Key Laboratory of Biomedical Imaging Science and System,Chinese Academy of Sciencesthe Alliance of International Science Organization(ANSO)under Grant No.2021A8017729010.
文摘Diabetes imposes a substantial burden on global healthcare systems.Worldwide,nearly half of individuals with diabetes remain undiagnosed,while conventional diagnostic techniques are often invasive,painful,and expensive.In this study,we propose a noninvasive approach for diabetes detection using photoplethysmography(PPG),which is widely integrated into modern wearable devices.First,we derived velocity plethysmography(VPG)and acceleration plethysmography(APG)signals from PPG to construct multi-channel waveform representations.Second,we introduced a novel multiset spatiotemporal feature fusion framework that integrates hand-crafted temporal,statistical,and nonlinear features with recursive feature elimination and deep feature extraction using a one-dimensional statistical convolutional neural network(1DSCNN).Finally,we developed an interpretable diabetes detection method based on XGBoost,with explainable artificial intelligence(XAI)techniques.Specifically,SHapley Additive exPlanations(SHAP)and Local InterpretableModel-agnostic Explanations(LIME)were employed to identify and interpret potential digital biomarkers associated with diabetes.To validate the proposed method,we extended the publicly available Guilin People’s Hospital dataset by incorporating in-house clinical data from ten subjects,thereby enhancing data diversity.A subject-independent cross-validation strategy was applied to ensure that the testing subjects remained independent of the training data for robust generalization.Compared with existing state-of-the-art methods,our approach achieved superior performance,with an area under the curve(AUC)of 80.5±15.9%,sensitivity of 77.2±7.5%,and specificity of 64.3±18.2%.These results demonstrate that the proposed approach provides a noninvasive,interpretable,and accessible solution for diabetes detection using PPG signals.
基金funded by the Deanship of Graduate Studies and Scientific Research at Qassim University for financial support(QU-APC-2025).
文摘Hemoglobin is a vital protein in red blood cells responsible for transporting oxygen throughout the body.Its accurate measurement is crucial for diagnosing and managing conditions such as anemia and diabetes,where abnormal hemoglobin levels can indicate significant health issues.Traditional methods for hemoglobin measurement are invasive,causing pain,risk of infection,and are less convenient for frequent monitoring.PPG is a transformative technology in wearable healthcare for noninvasive monitoring and widely explored for blood pressure,sleep,blood glucose,and stress analysis.In this work,we propose a hemoglobin estimation method using an adaptive lightweight convolutional neural network(HMALCNN)from PPG.The HMALCNN is designed to capture both fine-grained local waveform characteristics and global contextual patterns,ensuring robust performance across acquisition settings.We validated our approach on two multi-regional datasets containing 152 and 68 subjects,respectively,employing a subjectindependent 5-fold cross-validation strategy.The proposed method achieved root mean square errors(RMSE)of 0.90 and 1.20 g/dL for the two datasets,with strong Pearson correlations of 0.82 and 0.72.We conducted extensive posthoc analyses to assess clinical utility and interpretability.A±1 g/dL clinical error tolerance evaluation revealed that 91.3%and 86.7%of predictions for the two datasets fell within the acceptable clinical range.Hemoglobin range-wise analysis demonstrated consistently high accuracy in the normal and low hemoglobin categories.Statistical significance testing using the Wilcoxon signed-rank test confirmed the stability of performance across validation folds(p>0.05 for both RMSE and correlation).Furthermore,model interpretability was enhanced using Gradient-weighted Class Activation Mapping(Grad-CAM),supporting the model’s clinical trustworthiness.The proposed HMALCNN offers a computationally efficient,clinically interpretable,and generalizable framework for noninvasive hemoglobin monitoring,with strong potential for integration into wearable healthcare systems as a practical alternative to invasive measurement techniques.
文摘Background: Lower extremity Peripheral artery disease (PAD) is caused by atherosclerosis, or Plaque buildup, that reduces the blood flow to the legs and feet. PAD affects approximately 230 million adults worldwide and is associated with an increased risk of coronary heart disease, stroke, and leg amputation. The first-line method for diagnosis of PAD is the Ankle Brachial Index (ABI), which is the ratio of ankle to brachial higher systolic pressure measured in ankles and arms. The Toe Brachial Index (TBI), which is the ratio of the toe systolic pressure to brachial higher systolic pressure measured in both arms, is considered to be an alternative to the ABI in screening for PAD. The ABI and TBI are measured on the right and left side, and the lower of these numbers is the patient’s overall ABI and TBI. Clinical studies and meta-analysis reviews have shown that the conventional ABI measurement, which uses a cuff, and handheld sphygmomanometer and continuous-wave Doppler tracings, provides an acceptable-to-high specificity level but low sensitivity when compared with vascular color Doppler ultrasound, and/or angiography methods. Another study has shown that the TBI measurement has greater sensitivity but lower specificity than the ABI when compared with vascular color Doppler ultrasound diagnostic based on waveforms. The aim of this clinical study was to evaluate the specificity and sensitivity of the VasoPad System comparing its results to the vascular color doppler ultrasound waveforms. Materials and Methods: The VasoPad System is an automated device using the pulse wave method to measure the arms and ankles dorsalis and tibial posterior artery blood pressures, the photoplethysmography second derivative (PTGSD) to estimate the toe systolic pressure, a patented photoplethysmography (PTG) index marker and volume plethysmography via cuffs during deflation. Vascular Color Doppler ultrasound can diagnose stenosis through the direct visualization of atherosclerosis or plaques and through waveform analysis. The vascular color Doppler ultrasound provides 3 waveform types. The type 1, triphasic waveform is normal blood flow and no atherosclerosis or plaque, the type 2, diphasic waveform is seen when there are atherosclerosis plaques, but normal blood flow, and the type 3, monophasic waveform reflects stenosis with diameter reduction > 50%. Results: The sum of the overall ABI and TBI VasoPad values, called Sum of Brachial Indices (SBI), gave a specificity of 88.89% and sensitivity of 100% for detecting vascular color Doppler ultrasound biphasic and monophasic waveforms versus triphasic waveforms with a cutoff ≤ 1.36 (P Conclusion: The VasoPad was useful for detecting PAD, which is fully defined as having vessel stenosis > 50% (Doppler monophasic waveforms) but also early stage of atherosclerosis plaque of the lower extremities (Doppler biphasic waveforms). The VasoPad method provided a remarkable sensitivity of 100% and a specificity level similar to those of the conventional ABI test method compared with the vascular color Doppler ultrasound. In addition to being useful to screen and detect PAD, the VasoPad offers early detection of lower extremity atherosclerosis, with normal blood flow (Doppler biphasic waveforms), which could provide greater treatment options and thus reduce the overall number of lower extremity complications.
基金supported by the National Nature Science Foundation of China(Grant Number:61962010).
文摘Deepfake-generated fake faces,commonly utilized in identity-related activities such as political propaganda,celebrity impersonations,evidence forgery,and familiar fraud,pose new societal threats.Although current deepfake generators strive for high realism in visual effects,they do not replicate biometric signals indicative of cardiac activity.Addressing this gap,many researchers have developed detection methods focusing on biometric characteristics.These methods utilize classification networks to analyze both temporal and spectral domain features of the remote photoplethysmography(rPPG)signal,resulting in high detection accuracy.However,in the spectral analysis,existing approaches often only consider the power spectral density and neglect the amplitude spectrum—both crucial for assessing cardiac activity.We introduce a novel method that extracts rPPG signals from multiple regions of interest through remote photoplethysmography and processes them using Fast Fourier Transform(FFT).The resultant time-frequency domain signal samples are organized into matrices to create Matrix Visualization Heatmaps(MVHM),which are then utilized to train an image classification network.Additionally,we explored various combinations of time-frequency domain representations of rPPG signals and the impact of attention mechanisms.Our experimental results show that our algorithm achieves a remarkable detection accuracy of 99.22%in identifying fake videos,significantly outperforming mainstream algorithms and demonstrating the effectiveness of Fourier Transform and attention mechanisms in detecting fake faces.
基金supported by the Key Research Program of the Chinese Academy of Sciences(grant number ZDRW-ZS-2021-1-2).
文摘Pulse rate is one of the important characteristics of traditional Chinese medicine pulse diagnosis,and it is of great significance for determining the nature of cold and heat in diseases.The prediction of pulse rate based on facial video is an exciting research field for getting palpation information by observation diagnosis.However,most studies focus on optimizing the algorithm based on a small sample of participants without systematically investigating multiple influencing factors.A total of 209 participants and 2,435 facial videos,based on our self-constructed Multi-Scene Sign Dataset and the public datasets,were used to perform a multi-level and multi-factor comprehensive comparison.The effects of different datasets,blood volume pulse signal extraction algorithms,region of interests,time windows,color spaces,pulse rate calculation methods,and video recording scenes were analyzed.Furthermore,we proposed a blood volume pulse signal quality optimization strategy based on the inverse Fourier transform and an improvement strategy for pulse rate estimation based on signal-to-noise ratio threshold sliding.We found that the effects of video estimation of pulse rate in the Multi-Scene Sign Dataset and Pulse Rate Detection Dataset were better than in other datasets.Compared with Fast independent component analysis and Single Channel algorithms,chrominance-based method and plane-orthogonal-to-skin algorithms have a more vital anti-interference ability and higher robustness.The performances of the five-organs fusion area and the full-face area were better than that of single sub-regions,and the fewer motion artifacts and better lighting can improve the precision of pulse rate estimation.
基金The Scientic and Technological Research Council of Turkey(TUBITAK)under Grant No.113E610.
文摘Diabetes is a widespread and serious disease and noninvasive measurement has been in high demand.To address this problem,a power spectral density-based method was offered for determining glucose sensitive sub-bands in the nearinfrared(NIR)spectrum.The experiments were conducted using phantoms of different optical properties in-vitro conditions.The optical bands 1200–1300 nm and 2100–2200 nm were found feasible for measuring blood glucose.After that,a photoplethysmography(PPG)-based low cost and portable optical system was designed.It has six di®erent NIR wavelength LEDs for illumination and an InGaAs photodiode for detection.Optical density values were calculated through the system and used as independent variables for multiple linear regression analysis.The results of blood glucose levels for 24 known healthy subjects showed that the optical system prediction was nearly 80%in the A zone and 20%in the B zone according to the Clarke Error Grid analysis.It was shown that a promising easyuse,continuous,and compact optical system had been designed.
文摘Understanding the mechanisms of interaction between bone/bone marrow,circulatory system and nervous system is of great interest due to the potential clinical impact.In humans,the amount of knowledge in this domain remains relatively limited due to the extreme difficulty to monitor these tissues continuously,noninvasively and for long or repeated periods of time.A typical difficult task would be,for example,to continuously monitor bone/bone marrow blood perfusion,hemoglobin oxygen saturation or blood volume and study their dependence on the activity of the autonomic nervous system.In this review article,we want to show that nearinfrared light might be utilized to solve these problems in part.We hope that the present analysis will stimulate future studies in this domain,for which near-infrared light appears as the best available technology today.
基金National Natural Science Foundation of China(No.62271186)Anhui Key Project of Research and Development Plan(No.202104d07020005)。
文摘Deception detection plays a crucial role in criminal investigation.Videos contain a wealth of information regarding apparent and physiological changes in individuals,and thus can serve as an effective means of deception detection.In this paper,we investigate video-based deception detection considering both apparent visual features such as eye gaze,head pose and facial action unit(AU),and non-contact heart rate detected by remote photoplethysmography(rPPG)technique.Multiple wrapper-based feature selection methods combined with the K-nearest neighbor(KNN)and support vector machine(SVM)classifiers are employed to screen the most effective features for deception detection.We evaluate the performance of the proposed method on both a self-collected physiological-assisted visual deception detection(PV3D)dataset and a public bag-oflies(BOL)dataset.Experimental results demonstrate that the SVM classifier with symbiotic organisms search(SOS)feature selection yields the best overall performance,with an area under the curve(AUC)of 83.27%and accuracy(ACC)of 83.33%for PV3D,and an AUC of 71.18%and ACC of 70.33%for BOL.This demonstrates the stability and effectiveness of the proposed method in video-based deception detection tasks.
基金Supported by the National Natural Science Foundation of China(61903336,61976190)the Natural Science Foundation of Zhejiang Province(LY21F030015)。
文摘Background The use of remote photoplethysmography(rPPG)to estimate blood volume pulse in a noncontact manner has been an active research topic in recent years.Existing methods are primarily based on a singlescale region of interest(ROI).However,some noise signals that are not easily separated in a single-scale space can be easily separated in a multi-scale space.Also,existing spatiotemporal networks mainly focus on local spatiotemporal information and do not emphasize temporal information,which is crucial in pulse extraction problems,resulting in insufficient spatiotemporal feature modelling.Methods Here,we propose a multi-scale facial video pulse extraction network based on separable spatiotemporal convolution(SSTC)and dimension separable attention(DSAT).First,to solve the problem of a single-scale ROI,we constructed a multi-scale feature space for initial signal separation.Second,SSTC and DSAT were designed for efficient spatiotemporal correlation modeling,which increased the information interaction between the long-span time and space dimensions;this placed more emphasis on temporal features.Results The signal-to-noise ratio(SNR)of the proposed network reached 9.58dB on the PURE dataset and 6.77dB on the UBFC-rPPG dataset,outperforming state-of-the-art algorithms.Conclusions The results showed that fusing multi-scale signals yielded better results than methods based on only single-scale signals.The proposed SSTC and dimension-separable attention mechanism will contribute to more accurate pulse signal extraction.
文摘Forces acting on the skeleton could be divided into those originating from gravitational loading and those originating from muscle loading. Flat bones in a non-weight-baring segment of the skeleton probably experience forces mostly generated by muscle contractions. One purpose of muscle contractions is to generate blood flow within skeletal tissues. The present study aimed to investigate the pulsatile patellar bone blood flow after low and high intensity leg extension exercises. Forty-two healthy individuals volunteered for the study. Dynamic isotonic one leg extension/flexion exercises were performed in a leg extension machine. Randomly, the exercises were performed with the left or right leg with either 10 repetition maximum (10 RM) continuously without any resting periods (high intensity muscle work), or 20 RM with a 2 second rest between contractions (low intensity muscle work). The work load, expressed in kilograms totally lifted, was identical in both legs. The pulsatile patellar blood flow was recorded continuously using a photoplethysmographic technique. Blood pressure was measured continuously during muscle work by a non-invasive method (Finapress). The patellar pulsatile bone blood flow increased significantly more after high intensity muscle work (61%) compared to the same work load performed using a lower intensity (22%), p = 0.000073. Systolic blood pressure changed equally during and after both interventions. Post-exercise bone hyperaemia appears to be correlated to the intensity of muscle contractions in the muscle compartment attached to the bone.
基金funded by the National Key Research and Development Project(Nos.2021YFC3002204,2020YFC2003703,and 2020YFC1512304)the National Natural Science Foundation of China(Nos.62071451 and U21A20447)the CAMS Innovation Fund for Medical Sciences(No.2019-I2M-5-019).
文摘Accurate and portable Blood Pressure(BP)monitoring is vital for managing cardiovascular diseases.However,existing wearable continuous BP monitoring technologies are often inaccurate and rely on external calibration,limiting their practical application in continuous BP monitoring.To address this challenge,we have developed a Wearable continuous non-invasive BP Monitor(WeBPM)equipped with a finger cuff sensor,capable of monitoring BP continuously and accurately within medical-grade precision.WeBPM integrates advanced finger oscillographic BP measurement technology to provide reliable self-calibration functionality.Moreover,Pulsatile Cycle Volume Adjustment Method(PCVAM)we proposed for the closed-loop phase can continuously track changes in vasomotor tone under a controlled frequency based on pulsatile cycles,thereby enabling continuous BP measurement.In comparative experiments with the Nexfin monitor,WeBPM demonstrates excellent performance in induced dynamic BP experiments,with measurement errors of(–1.4±6.24)mmHg for Systolic BP(SBP)and(–0.82±4.83)mmHg for Diastolic BP(DBP).Additionally,compared to clinical invasive reference measurements,WeBPM’s SBP and DBP measurement errors are(–1.74±4.9)mmHg and(0.37±3.28)mmHg,respectively,further proving its outstanding performance.These results highlight WeBPM’s potential in personalized health management and remote monitoring,offering a new solution for continuous non-invasive BP monitoring.
