DistancePPG: Robust non-contact vital signs monitoring using a camera

TL;DR

DistancePPG introduces a novel camera-based vital sign estimation method that enhances accuracy across challenging conditions by adaptively combining signals from facial regions based on skin perfusion and lighting, expanding non-contact health monitoring capabilities.

Contribution

It proposes a new weighted averaging approach for combining facial skin signals, with an automatic method for weight determination, improving robustness of non-contact vital sign monitoring in diverse scenarios.

Findings

Improved signal-to-noise ratio in vital sign estimation.

Reduced error in pulse and breathing rate measurements.

Enhanced performance across different skin tones and lighting conditions.

Abstract

Vital signs such as pulse rate and breathing rate are currently measured using contact probes. But, non-contact methods for measuring vital signs are desirable both in hospital settings (e.g. in NICU) and for ubiquitous in-situ health tracking (e.g. on mobile phone and computers with webcams). Recently, camera-based non-contact vital sign monitoring have been shown to be feasible. However, camera-based vital sign monitoring is challenging for people with darker skin tone, under low lighting conditions, and/or during movement of an individual in front of the camera. In this paper, we propose distancePPG, a new camera-based vital sign estimation algorithm which addresses these challenges. DistancePPG proposes a new method of combining skin-color change signals from different tracked regions of the face using a weighted average, where the weights depend on the blood perfusion and incident light intensity in the region, to improve the signal-to-noise ratio (SNR) of camera-based estimate. One of our key contributions is a new automatic method for determining the weights based only on the video recording of the subject. The gains in SNR of camera-based PPG estimated using distancePPG translate into reduction of the error in vital sign estimation, and thus expand the scope of camera-based vital sign monitoring to potentially challenging scenarios. Further, a dataset will be released, comprising of synchronized video recordings of face and pulse oximeter based ground truth recordings from the earlobe for people with different skin tones, under different lighting conditions and for various motion scenarios.