Contactless biometric screening uses AI-powered remote photoplethysmography (rPPG) to measure vital signs including heart rate, heart rate variability, blood oxygen levels, and stress indicators from video analysis of facial skin color changes. The technology analyzes subtle color variations in facial skin caused by blood flow, extracting cardiovascular and respiratory information without any physical contact or wearable devices. AI algorithms process the video data to filter out noise, account for lighting variations, and extract accurate biometric measurements.
The contactless approach makes health monitoring accessible in diverse non-clinical settings including public facilities, corporate wellness programs, airports, schools, and smart city environments. The technology enables large-scale health screening, early detection of health issues, and population health monitoring without requiring individuals to wear devices or visit medical facilities. Applications include fever screening, stress monitoring in workplaces, general wellness checks, and public health surveillance. The technology is particularly valuable for screening large numbers of people quickly, identifying individuals who may need further medical evaluation, and providing convenient health monitoring in everyday environments. This democratizes access to basic health screening and enables proactive health management.
Provides a video-based health monitoring SDK that extracts vital signs (HR, HRV, SpO2, respiration) from a smartphone camera feed.
Developers of Anura, an AI platform that measures blood pressure, heart rate, and stress levels via 30-second video selfies using Transdermal Optical Imaging.
A medical-grade contactless vital signs monitor that measures blood pressure, pulse, and respiration using a device's camera.
Software solution that uses smartphone cameras (optical sensors) to measure blood pressure via fingertip placement.
An AI startup spun out of National Chiao Tung University that develops video-based contactless vital sign measurement software.
Develops CE-certified heart rhythm monitoring technology using smartphone cameras to detect Atrial Fibrillation (AFib).
Home of the Affective Computing research group led by Rosalind Picard.
Creators of Shen.AI, a computer vision technology for remote vital sign monitoring and health screening via smartphone cameras.
Develops Vitals, a camera-based health monitoring software using rPPG for wellness and telemedicine applications.
Provides an API that extracts physiological data from video feeds using rPPG, focused on corporate wellness and telehealth.
Creators of CausalImpact, a package for causal inference using Bayesian structural time-series.
Paper
Remote photoplethysmography for health assessment: a review informed by IntelliProve technologyFrontiers in Digital Health · Jan 5, 2026
A comprehensive review of remote photoplethysmography (rPPG) for assessing biomarkers like heart rate, respiration rate, and oxygen saturation using standard cameras, highlighting its application in accessible health monitoring.
Paper
VitalLens 2.0: High-Fidelity rPPG for Heart Rate Variability Estimation from Face VideoarXiv · Oct 30, 2025
Introduction of VitalLens 2.0, a deep learning model trained on 1,413 individuals that significantly improves the accuracy of estimating heart rate variability (HRV) and respiratory rate from face video.
Article
Facial video photoplethysmography for measuring average and instantaneous heart rate: a pilot validation studyShen.AI · Dec 4, 2025
A pilot validation study demonstrating that facial video photoplethysmography (vPPG) can measure heart rate with accuracy comparable to ECG, achieving a mean absolute error of 0.1 bpm for 60-second windows.
Article
Estimating heart rate variability using facial video photoplethysmography: a pilot validation studyShen.AI · Apr 4, 2025
Validation of HRV estimation from 1-minute face scans using vPPG, showing strong correlation (r = 0.98 for SDNN) with ECG readings.
