Efficient Real-Time Camera Based Estimation of Heart Rate and Its Variability

TL;DR

This paper presents a real-time, camera-based method for accurately estimating heart rate and variability without training, using novel filtering and motion suppression, validated on multiple datasets.

Contribution

A new real-time rPPG pipeline with innovative filtering and motion suppression that improves heart rate and HRV estimation without requiring training.

Findings

Achieves state-of-the-art results on public datasets

Operates in real-time without rPPG-specific training

Accurately estimates heart rate and variability in realistic conditions

Abstract

Remote photo-plethysmography (rPPG) uses a remotely placed camera to estimating 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 rate more accurately, but also extracts the pulse waveform to time heart beats and measure heart rate variability. This method requires no rPPG specific training and is able to operate in real-time. We validate our method on a self-recorded dataset under an idealized lab setting, and show state-of-the-art results on two public dataset with realistic conditions (VicarPPG and PURE).