Jing Wei Chin

BACKGROUND Vital sign measurement is essential to assess a patient’s physiological status to guide clinical decisions on triage and resuscitation in the emergency department (ED). Manual measurement contributes to bottlenecks and delays in the triage and care processes when a surge in service demand or manpower shortage occurs. Remote photoplethysmography (rPPG) enables contactless vital sign estimation by analyzing changes in light reflected from the skin in smartphone-captured facial videos caused by pulsatile changes in tissue blood volume and light absorption. rPPG has the potential to automate vital sign measurement. However, its accuracy in the ED remains unclear. OBJECTIVE We aimed to compare the accuracy of rPPG–based contactless heart rate (HR) estimation with manual measurement in the ED and assess patient satisfaction and comfort with different measurement methods. METHODS This was a prospective cross-sectional study on a convenience sample of ambulatory adult patients in a tertiary ED in Hong Kong from October to November 2024. The reference standards were manual HR measurements by a research nurse using a standard hospital device. Simultaneously, 25-second facial videos were recorded with an iPhone for contactless HR estimation using a proprietary convolutional neural network algorithm. The accuracy of the contactless method was assessed using the intraclass correlation coefficient (ICC), mean absolute error (MAE), root-mean-square error (RMSE), and Bland–Altman plot, with ± 5 beats per minute (bpm) predefined as clinically acceptable levels of agreement. Patient satisfaction and comfort, rated on a 0-100 mm visual analog scale (VAS), were compared between the two methods using the Wilcoxon signed-rank test. RESULTS We analyzed 478 videos from 161 patients, including 97 women and 64 men. The mean patient age was 55.0 (SD 16.6) years, and 73.9% were Chinese (119/161) with Fitzpatrick skin types of 3 (61.5%, 99/161) and 4 (34.8%, 56/161). The contactless method had an ICC of 0.991 (95% CI 0.987-0.993), a MAE of 1.38 beats per minute (bpm), and a RMSE of 1.82 bpm. The Bland–Altman plot showed a bias of 0.54 bpm (95% CI –2.89 to 3.97 bpm), which falls within the predefined clinically acceptable levels of agreement. Patient satisfaction (contactless VAS 95.4 mm vs. manual VAS 90.3 mm, P<.001) and comfort (contactless VAS 98.0 mm vs. manual VAS 87.0 mm, P<.001) were significantly higher with the contactless method compared to manual measurement. CONCLUSIONS rPPG–based HR estimation from smartphone-captured facial videos is accurate in the ED. Further validation studies involving broader patient populations with a wider range of vital signs and skin colors are warranted. Future research should focus on extending the application of rPPG technology to other vital signs to fully realize its potential in automating vital sign measurements. CLINICALTRIAL ClinicalTrials.gov NCT06536647; https://clinicaltrials.gov/study/NCT06536647

Background: Cardiovascular disease (CVD) is the leading cause of mortality worldwide, creating demand for continuous, unobtrusive monitoring solutions. This clinical validation evaluates the accuracy of remote photoplethysmography (rPPG), a contactless method using camera video, for measuring pulse rate (PR) in patients with CVD. Methods: We enrolled 50 adults with confirmed CVD at a clinical trial center. In a 6 min rested session, synchronized facial video (under controlled lighting), electrocardiogram (ECG), and photoplethysmography (PPG) signals were recorded. PR was derived from 25 s video segments using rPPG-enabled software and compared to ECG-derived PR via regression and Bland–Altman analysis. Results: Data from 47 participants (n = 817 samples) were analyzed. rPPG-derived PR showed strong agreement with ECG, with a mean absolute error of 1.061 bpm, root-mean-squared error of 2.845 bpm, and Pearson correlation of 0.962. Mixed-effects regression analyses (after 2% outlier removal, n = 782) indicated minimal influence from demographic, environmental, or CVD factors on accuracy. PPG-ECG discrepancies reflected inherent methodological differences. Conclusion: The rPPG method provides accurate, contactless PR monitoring in CVD patients, supporting its potential for remote patient monitoring and early deterioration detection. Future work will validate rPPG for irregular rhythms, additional vital signs, and diverse cohorts to strengthen clinical robustness for cardiometabolic risk assessment.

Remote photoplethysmography (rPPG) is an emerging non-contact method for monitoring cardiovascular health based on facial videos. The quality of the captured videos largely determines the efficacy of rPPG in this application. Traditional rPPG techniques, while effective for heart rate (HR) estimation, often produce signals with an inadequate signal-to-noise ratio (SNR) for reliable vital sign measurement due to artifacts like head motion and measurement noise. Another pivotal factor is the overlooking of the inherent properties of signals generated by rPPG (rPPG-signals). To address these limitations, we introduce DiffPhys, a novel deep generative model particularly designed to enhance the SNR of rPPG-signals. DiffPhys leverages the conditional diffusion model to learn the distribution of rPPG-signals and uses a refined reverse process to generate rPPG-signals with a higher SNR. Experimental results demonstrate that DiffPhys elevates the SNR of rPPG-signals across within-database and cross-database scenarios, facilitating the extraction of cardiovascular metrics such as HR and HRV with greater precision. This enhancement allows for more accurate monitoring of health conditions in non-clinical settings.

Blood oxygen saturation (SpO₂) is an essential physiological parameter for evaluating a person’s health. While conventional SpO₂ measurement devices like pulse oximeters require skin contact, advanced computer vision technology can enable remote SpO₂ monitoring through a regular camera without skin contact. In this paper, we propose novel deep learning models to measure SpO₂ remotely from facial videos and evaluate them using a public benchmark database, VIPL-HR. We utilize a spatial–temporal representation to encode SpO₂ information recorded by conventional RGB cameras and directly pass it into selected convolutional neural networks to predict SpO₂. The best deep learning model achieves 1.274% in mean absolute error and 1.71% in root mean squared error, which exceed the international standard of 4% for an approved pulse oximeter. Our results significantly outperform the conventional analytical Ratio of Ratios model for contactless SpO₂ measurement. Results of sensitivity analyses of the influence of spatial–temporal representation color spaces, subject scenarios, acquisition devices, and SpO₂ ranges on the model performance are reported with explainability analyses to provide more insights for this emerging research field.

Remote Photoplethysmography (rPPG) is a contactless method that enables the detection of various physiological signals from facial videos. rPPG utilizes a digital camera to detect subtle changes in skin color to measure vital signs such as heart rate variability (HRV), an important biomarker related to the autonomous nervous system. This paper presents a novel contactless HRV extraction algorithm, WaveHRV, based on the Wavelet Scattering Transform technique, followed by adaptive bandpass filtering and inter-beat-interval (IBI) analysis. Furthermore, a novel method is introduced to preprocess noisy contact-based PPG signals. WaveHRV is bench-marked against existing algorithms and public datasets. Our results show that WaveHRV is promising and achieves the lowest mean absolute error (MAE) of 10.5 ms and 6.15 ms for RMSSD and SDNN on the UBFCrPPG dataset.

Blood pressure (BP) determines whether a person has hypertension and offers implications as to whether he or she could be affected by cardiovascular disease. Cuff-based sphygmomanometers have traditionally provided both accuracy and reliability, but they require bulky equipment and relevant skills to obtain precise measurements. BP measurement from photoplethysmography (PPG) signals has become a promising alternative for convenient and unobtrusive BP monitoring. Moreover, the recent developments in remote photoplethysmography (rPPG) algorithms have enabled new innovations for contactless BP measurement. This paper illustrates the evolution of BP measurement techniques from the biophysical theory, through the development of contact-based BP measurement from PPG signals, and to the modern innovations of contactless BP measurement from rPPG signals. We consolidate knowledge from a diverse background of academic research to highlight the importance of multi-feature analysis for improving measurement accuracy. We conclude with the ongoing challenges, opportunities, and possible future directions in this emerging field of research.

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