Research
While we are not the pioneers of effective AI or TOI, we are one of its trailblazers. Guided by rigorous science and cutting-edge clinical research, we stand at the forefront of transforming how technology intersects with human health and emotions. Our innovations don’t just adapt to industry trends; they redefine the possibilities of personalized wellness care. By combining the power of AI with evidence-based methodologies, we deliver solutions that inspire confidence, enhance personal outcomes, and shape the future of wellness. Welcome to the next generation of fitness innovation.
Clinical Studies
Transdermal Optical Imaging Reveal Basal Stress via Heart Rate Variability Analysis: A Novel Methodology Comparable to Electrocardiography
The present study examined the validity of a novel physiological measurement technology called transdermal optical imaging (TOI) technology at assessing basal stress. This technology conveniently, contactlessly, and remotely measures facial blood flow changes using a conventional digital video camera. We compared data from TOI against the pulse data collected from the FDA approved BIOPAC system. One hundred thirty-six healthy adults participated in the study. We found that TOI measurements of heart rate and heart rate variability, which reflects basal stress, corresponded strongly to those obtained from BIOPAC. These findings indicate that transdermal optical imaging technology is a viable method to monitor heart rate and heart rate variability not only accurately but also conveniently, contactlessly, and remotely. Further, measures of heart rate variability obtained via transdermal optical imaging serves as a valid index of basal stress. Potential applications of this technology in psychological research and other fields are discussed.
Exploring Contactless Vital Signs Collection in Video Telehealth Visits Among Veterans Affairs Providers and Patients: Pilot Usability Study
To expand veterans’ access to health care, the Veterans Affairs (VA) Office of Connected Care explored a novel software feature called “Vitals” on its VA Video Connect telehealth platform. Vitals uses contactless, video-based, remote photoplethysmography (rPPG) through the infrared camera on veterans’ smartphones (and other devices) to automatically scan their faces to provide real-time vital statistics on screen to both the provider and patient. Objective: This study aimed to assess VA clinical provider and veteran patient attitudes regarding the usability of Vitals.
Smartphone-Based Blood Pressure Measurement Using Transdermal Optical Imaging Technology
Cuff-based blood pressure measurement lacks comfort and convenience. Here, we examined whether blood pressure can be determined in a contactless manner using a novel smartphone-based technology called transdermal optical imaging. This technology processes imperceptible facial blood flow changes from videos captured with a smartphone camera and uses advanced machine learning to determine blood pressure from the captured signal.
Importance of general adiposity, visceral adiposity and vital signs in predicting blood biomarkers using machine learning
Blood biomarkers are measured for their ability to characterise physiological and disease states. Much is known about linear relations between blood biomarker concentrations and individual vital signs or adiposity indexes (eg, BMI). Comparatively little is known about non-linear relations with these easily accessible features, particularly when they are modelled in combination and can potentially interact with one another.
Remote Photoplethysmography Is an Accurate Method to Remotely Measure Respiratory Rate: A Hospital-Based Trial
Remote photoplethysmography imaging (rPPG) is a new solution proposed to measure vital signs, such as respiratory rate (RR) in teleconsultation, by using a webcam. The results, presented here, aim at evaluating the accuracy of such remote measurement methods, compared with existing measurement methods, in a real-life clinical setting. For each patient, measurement of RR, using the standard system (control), has been carried out concomitantly with the experimental system. A 60-s time frame was used for the measurements made by our rPPG system. Age, gender, BMI, and skin phototype were collected. We performed the intraclass correlation coefficient and Bland–Altman plot to analyze the accuracy and precision of the rPPG algorithm readings. Measurements of RR, using the two methods, have been realized on 963 patients. Comparison of the two techniques showed excellent agreement (96.0%), with most of the patients (n = 924—standard patients) being in the confidence interval of 95% in Bland–Altman plotting. There were no significant differences between standard patients and outlier patients for demographic and clinical characteristics. This study indicates a good agreement between the rPPG system and the control, thus allowing clinical use of this remote assessment of the respiratory rate.
Research Publications
Transdermal optical imaging revealed diferent spatiotemporal patterns of facial cardiovascular activities
Human cardiovascular activities are important indicators of a variety of physiological and psychological activities in human neuroscience research. The present proof-of-concept study aimed to reveal the spatiotemporal patterns of cardiovascular activities from the dynamic changes in hemoglobin concentrations in the face. We frst recorded the dynamics of facial transdermal blood fow using a digital video camera and the Electrocardiography (ECG) signals using an ECG system simultaneously. Then we decomposed the video imaging data extracted from diferent sub-regions of a face into independent components using group independent component analysis (group ICA). Finally, the ICA components that included cardiovascular activities were identifed by correlating their magnitude spectrum to those obtained from the ECG. We found that cardiovascular activities were associated with fve independent components refecting diferent spatiotemporal dynamics of facial blood fow changes. The strongest strengths of these ICA components were observed in the bilateral forehead, the left chin, and the left cheek, respectively. Our fndings suggest that the cardiovascular activities presented diferent dynamic properties within diferent facial sub-regions, respectively. More broadly, the present fndings point to the potential of the transdermal optical imaging technology as a new neuroscience methodology to study human physiology and psychology, noninvasively and remotely in a contactless manner.
Transdermal Optical Imaging Reveal Basal Stress via Heart Rate Variability Analysis: A Novel Methodology Comparable to Electrocardiography
The present study examined the validity of a novel physiological measurement technology called transdermal optical imaging (TOI) technology at assessing basal stress. This technology conveniently, contactlessly, and remotely measures facial blood flow changes using a conventional digital video camera. We compared data from TOI against the pulse data collected from the FDA approved BIOPAC system. One hundred thirty-six healthy adults participated in the study. We found that TOI measurements of heart rate and heart rate variability (HRV), which reflects basal stress, corresponded strongly to those obtained from BIOPAC. These findings indicate that TOI technology is a viable method to monitor heart rate and HRV not only accurately but also conveniently, contactlessly, and remotely. Further, measures of HRV obtained via TOI serves as a valid index of basal stress. Potential applications of this technology in psychological research and other fields are discussed.
Measuring Blood Pressure: from Cuff to Smartphone
For measurement of blood pressure, using inflatable cuff-based technology can be inconvenient, uncomfortable, and requires special equipment. These issues could be overcome by using a contactless technology that measures blood pressure with the ubiquitous smartphone. Recent Findings In a proof of concept study involving normotensive participants, Luo et al. demonstrated brachial blood pressure measurements from video of the face with accuracy comparable to traditional automated blood pressure monitors. Summary There is still some way to go before contactless blood pressure measurement technology is sufficiently accurate and robust for clinical use. For example, variations in skin tone and lighting conditions must be addressed. Further, new predictive features will be necessary to reveal added information about blood pressure and thus improve prediction accuracy. New tools are likely to encourage blood pressure measurements in more people, in more places, and with more regularity than ever before.
Heart rate variability analysis: physiological foundations and main methods
The article presents the main provisions of the methodology for the analysis of heart rate variability (HRV), which is now actively and widely implemented in many fields of medicine and applied physiology. This methodology was first developed in space medicine, where, already during the first manned spaceflights, there was a need in operative assessment of the person’s reactions and abilities to maintain high performance and good level of health under different stress conditions. The HRV analysis methodology is based on the measurement of a consecutive series of cardiac cycle durations, for which electrocardiography, rheocardiography, ballistic cardiography, etc., can be used. The resulting numerical series are subjected to mathematical analysis using statistical, spectral and other methods. The results are interpreted as medical and physiological criteria of the functional state of the organism. Based on the mathematical model, a probabilistic approach to the prediction of pathological conditions was proposed. Indicators of the stress degree of regulatory systems and their functional reserve, which are calculated from the HRV analysis data, are used in the mathematical model of the functional states. In order to obtain the decision rules for the recognition of identified classes of functional states the stepwise discriminant analysis has been applied. Equations of the discriminant function were obtained. This article examines in detail this new probabilistic approach to the HRV analysis and provides examples of its use for assessing the functional state of cosmonauts at various stages of long space flights.
Estimation of vital signs from facial videos via video magnification and deep learning
The continuous monitoring of vital signs is one of the hottest topics in healthcare. Recent technological advances in sensors, signal processing, and image processing spawned the development of nocontact techniques such as remote photoplethysmography (rPPG). To solve the common problems of rPPG including weak extracted signals, body movements, and generalization with limited data resources, we proposed a dual-path estimation method based on video magnification and deep learning. First, image processes are applied to detect, track, and magnificate facial ROIs automatically. Then, the steady part of the wave of each processed ROI is used for the extraction of features including heart rate, PTT, and features of pulse wave waveform. The blood pressures are estimated from the features via a small CNN. Results comply with the current standard and promise potential clinical applications in the future.
A machine learning-based approach for constructing remote photoplethysmogram signals from video cameras
Advancements in health monitoring technologies are increasingly relying on capturing heart signals from video, a method known as remote photoplethysmography (rPPG). This study aims to enhance the accuracy of rPPG signals using a novel computer technique. We developed a machine-learning model to improve the clarity and accuracy of rPPG signals by comparing them with traditional photoplethysmogram (PPG) signals from sensors. The model was evaluated across various datasets and under different conditions, such as rest and movement. Evaluation metrics, including dynamic time warping (to assess timing alignment between rPPG and PPG) and correlation coefficients (to measure the linear association between rPPG and PPG), provided a robust framework for validating the effectiveness of our model in capturing and replicating physiological signals from videos accurately. Our method showed significant improvements in the accuracy of heart signals captured from video, as evidenced by dynamic time warping and correlation coefficients. The model performed exceptionally well, demonstrating its effectiveness in achieving accuracy comparable to direct-contact heart signal measurements. This study introduces a novel and effective machine-learning approach for improving the detection of heart signals from video. The results demonstrate the flexibility of our method across various scenarios and its potential to enhance the accuracy of health monitoring applications, making it a promising tool for remote healthcare.
Assessment of ROI Selection for Facial Video-Based rPPG
In general, facial image-based remote photoplethysmography (rPPG) methods use color based and patch-based region-of-interest (ROI) selection methods to estimate the blood volume pulse (BVP) and beats per minute (BPM). Anatomically, the thickness of the skin is not uniform in all areas of the face, so the same diffuse reflection information cannot be obtained in each area. In recent years, various studies have presented experimental results for their ROIs but did not provide a valid rationale for the proposed regions. In this paper, to see the effect of skin thickness on the accuracy of the rPPG algorithm, we conducted an experiment on 39 anatomically divided facial regions. Experiments were performed with seven algorithms (CHROM, GREEN, ICA, PBV, POS, SSR, and LGI) using the UBFC-rPPG and LGI-PPGI datasets considering 29 selected regions and two adjusted regions out of 39 anatomically classified regions. We proposed a BVP similarity evaluation metric to find a region with high accuracy. We conducted additional experiments on the TOP-5 regions and BOT-5 regions and presented the validity of the proposed ROIs. The TOP-5 regions showed relatively high accuracy compared to the previous algorithm’s ROI, suggesting that the anatomical characteristics of the ROI should be considered when developing a facial image-based rPPG algorithm.