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.

翻译：⼼率是⼈类最重要的健康指标之⼀，可⽤于探究和深⼊理解⼈体多种⽣理与⼼理信息。摆脱接触式传感器的束缚来估计⼼率，因⽽成为⼀个极具吸引⼒的研究领域，因为它能够在更⼴泛的场景中实现健康监测。为此，学界已开发出多种基於摄像头的⼼率估计算法，涵盖从经典图像处理到复杂的深度学习模型与架构。此类研究的核⼼在于健康与视觉数据的采集、清洗、转换与标注。本⽂探讨如何为开发或测试基于⾯部区域图像估计⼼率的算法或机器学习模型⽽准备数据。所准备的数据应包含摄像头帧以及⼼电图传感器的读数。所提出的流⽔线分为四个主要步骤，即故障数据剔除、帧与⼼电图时间戳去抖动、信号去噪与滤波，以及帧标注⽣成。我们的主要贡献在于⼀种消除健康传感器与摄像头时间戳抖动的创新技术，以及⼀种精确对齐视觉帧与⼼电图传感器数据（该⽅法同样适⽤于其他传感器类型）的时间同步⽅法。