Facial Video Analytics: An Intelligent Approach to Heart Rate Estimation Using AI Framework

The development of remote sensing of vital signs, which use a camera instead of a skin-contact sensor, has improved the measurement environment. Two ideas form the foundation of the camera-based approach: colour and motion. Remote ballistocardiography (RBCG), a motion-based technique, examines the minute movements of the head caused by heartbeat, whereas remote photoplethysmography (RPPG), a color-based technique, measures the colour variation of the face caused by blood reflection. Accurate heart rate monitoring is vital to collecting valuable physical data from human participants. Because of its ease of use and affordability, face video-based heart rate estimate has gained a lot of attention lately. Its precision, dependability, and computing complexity, however, still fall short of expectations. Our goal in this work has been to create a novel deep learning system that use RGB camera to estimate heart rates in real time. The aim of this research topic is to generating an AI algorithm for Heart Rate monitoring from facial video data and integrate it into a real-time heart rate monitoring system. The system will be designed to be user-friendly and easily deployable in various settings such as hospitals, clinics, and homes.