Abstract

Heart rate (HR) ranks among the most critical physiological indicators in the human body, significantly illuminating an individual’s state of physical health. Distinguished from traditional contact-based heart rate measurement, the utilization of Remote Photoplethysmography (rPPG) for remote heart rate monitoring eliminates the need for skin contact, relying solely on a camera for detection. This non-contact measurement method has emerged as an increasingly noteworthy research area. With the rapid development of deep learning, new technologies in this area have spurred the emergence of many new rPPG methods for HR measurement. However, comprehensive review papers in this field are scarce. Consequently, this paper aims to provide a comprehensive overview centered around rPPG methods employed for the purpose of heart rate measurement. We systematically organized the existing rPPG methods, with a specific focus on those based on deep learning, and described and analyzed the structures and key aspects of these methods. Additionally, we summarized the datasets and tools used for related research and compiled the performance of different methods on prominent datasets. Finally, this paper discusses the current research barriers in rPPG methods, as well as the latest practical applications and potential future directions for development. We hope that this review will help researchers quickly understand this field and promote the exploration of more unknown challenges.