Monitoring HRV with a 1-minute face scan – a validation study

Can heart rate variability (HRV) be measured reliably with just a face scan? A new validation study shows that it’s possible—and with impressive accuracy. Researchers from the Polish Academy of Sciences and Wroclaw Medical University tested Shen AI’s video PPG technology against ECG readings, focusing on two key time-domain HRV metrics.

Key insights

  • Strong correlation with ECG: HRV metrics estimated via Shen.AI’s facial vPPG technology strongly aligned with ECG results (r = 0.98 for SDNN and r = 0.88 for lnRMSSD).
  • High accuracy: The mean absolute percentage error (MAPE) was 11.0% for SDNN and 7.3% for lnRMSSD—well within acceptable limits for clinical applications.
  • Works with 1-minute scans: This study confirms that short-term HRV estimation from a 1-minute face scan is possible—even for metrics traditionally captured over 5 minutes.

Research introduction

The following is an excerpt of the “Estimating heart rate variability using facial video photoplethysmography: a pilot validation study” paper.

Video photoplethysmography (vPPG), also known as remote or imaging photoplethysmography, is an optical technique of recording blood pulsations in the skin vasculature, which can be used to estimate vital signs or other physiological parameters. Specifically, vPPG enables recording of fluctuations in the intensity of ambient light reflected from the skin caused by changes in the amount of light absorbed by the blood in the superficial vessels due to their cyclic pulsation. These fluctuations are invisible to the naked eye but usually strong enough to be detected by a digital camera. vPPG provides therefore the possibility of recording PPG signals in a contactless manner without the need of special light sources and using a camera as a light sensor. This makes it possible to use cameras built into mobile devices, such as smartphones, tablets, or laptops, to measure or monitor various physiological parameters.

Similar to standard PPG signals, vPPG signals can be used to estimate the time intervals between successive blood pulsations, which are relatively similar to the time intervals between heartbeats (although not exactly the same). These time intervals can be subsequently used to calculate (estimate) heart rate (HR) as well as various indices of heart rate variability (HRV) describing the variability of the time intervals between successive heartbeats. HRV is a well-known marker of autonomic nervous system activity with various applications. A reduced HRV indicates a less adaptive autonomic regulation of the heart (i.e. an impaired ability to respond to various stimuli) and is an independent risk factor for cardiovascular events and mortality. Low HRV is also associated with higher risk for developing hypertension or cardiovascular disease.

HRV can also be used to predict the risk of various adverse events, for example the risk of myocardial ischemia in patients without known coronary artery disease, the risk of renal function deterioration in patients with chronic kidney disease, or the risk of falls. Beyond clinical applications, HRV can be used to monitor and manage the training and recovery process in athletes, particularly in endurance sports, such as long-distance running, swimming, cycling, rowing, or cross-country skiing, but also in other sports.

In this study, we investigated HRV indices estimated using facial vPPG technology called Shen.AI Vitals developed by Shen AI (Tallinn, Estonia). This technology employs mobile device cameras to acquire 1-min vPPG signals from several regions of the face (by detecting pulsatile changes in the intensity of ambient light reflected from the facial skin) and then analyses these signals to estimate various physiological parameters, including the HRV index -– SDNN, i.e. the standard deviation of the time intervals between successive normal heartbeats(default), or lnRMSSD, i.e. the natural logarithm of the root mean square of successive differences in time intervals between heartbeats (optional).

SDNN is the most common HRV index in the time domain and captures the total variability in inter-beat time intervals in the given time period (quantified by their standard deviation), thus providing an overall measure of current cardiac and autonomic nervous system activity. In turn, RMSSD quantifies the variability of successive inter-beat time intervals, thus focusing on highfrequency HR oscillations, reflecting mostly (albeit not exclusively) the activity of the parasympathetic nervous system. Taking the logarithm of HRV indices is a common method to achieve a more normal distribution. In particular, the natural logarithm of RMSSD (lnRMSSD) is often used to monitor the vagal-related adaptation to physical training and post-training recovery.

The aim of our study was to investigate the accuracy and precision of SDNN and lnRMSSD values estimated by the Shen.AI Vitals algorithms based on vPPG signals recorded with a smartphone camera, as compared with reference values obtained from simultaneously-recorded electrocardiograms (ECG).

Research details

Title: Estimating heart rate variability using facial video photoplethysmography: a pilot validation study
Authors: Leszek Pstras, Tymoteusz Okupnik, Beata Ponikowska, Bartlomiej Paleczny
Institutions: Nalecz Institute of Biocybernetics and Biomedical Engineering, Polish Academy of Sciences & Wroclaw Medical University
Published February 16, 2025

Read the full study here.