  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.
