Real-time Webcam Heart-Rate and Variability Estimation with Clean Ground Truth for Evaluation

TL;DR

This paper presents a real-time, unsupervised rPPG method for accurate heart rate and variability estimation, introducing a new dataset with verified ground truth for improved benchmarking.

Contribution

The authors develop a novel, efficient rPPG pipeline capable of real-time HR and HRV estimation without training, and provide a new dataset with verified annotations for better evaluation.

Findings

Achieves state-of-the-art accuracy in HR and HRV estimation

Operates in real-time without supervised training

Provides a new dataset with verified ground truth annotations

Abstract

Remote photo-plethysmography (rPPG) uses a camera to estimate a person's heart rate (HR). Similar to how heart rate can provide useful information about a person's vital signs, insights about the underlying physio/psychological conditions can be obtained from heart rate variability (HRV). HRV is a measure of the fine fluctuations in the intervals between heart beats. However, this measure requires temporally locating heart beats with a high degree of precision. We introduce a refined and efficient real-time rPPG pipeline with novel filtering and motion suppression that not only estimates heart rates, but also extracts the pulse waveform to time heart beats and measure heart rate variability. This unsupervised method requires no rPPG specific training and is able to operate in real-time. We also introduce a new multi-modal video dataset, VicarPPG 2, specifically designed to evaluate rPPG algorithms on HR and HRV estimation. We validate and study our method under various conditions on a comprehensive range of public and self-recorded datasets, showing state-of-the-art results and providing useful insights into some unique aspects. Lastly, we make available CleanerPPG, a collection of human-verified ground truth peak/heart-beat annotations for existing rPPG datasets. These verified annotations should make future evaluations and benchmarking of rPPG algorithms more accurate, standardized and fair.