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.
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