  Heart rate is one of the most vital health metrics which can be utilized to
investigate and gain intuitions into various human physiological and
psychological information. Estimating heart rate without the constraints of
contact-based sensors thus presents itself as a very attractive field of
research as it enables well-being monitoring in a wider variety of scenarios.
Consequently, various techniques for camera-based heart rate estimation have
been developed ranging from classical image processing to convoluted deep
learning models and architectures. At the heart of such research efforts lies
health and visual data acquisition, cleaning, transformation, and annotation.
In this paper, we discuss how to prepare data for the task of developing or
testing an algorithm or machine learning model for heart rate estimation from
images of facial regions. The data prepared is to include camera frames as well
as sensor readings from an electrocardiograph sensor. The proposed pipeline is
divided into four main steps, namely removal of faulty data, frame and
electrocardiograph timestamp de-jittering, signal denoising and filtering, and
frame annotation creation. Our main contributions are a novel technique of
eliminating jitter from health sensor and camera timestamps and a method to
accurately time align both visual frame and electrocardiogram sensor data which
is also applicable to other sensor types.
