Heartbeat Rate Estimation using Convolutional Neural Network

Heartbeat rate estimation using Convolutional Neural Network presents an innovative and non-invasive solution for cardiovascular monitoring. This approach harnesses computer vision and signal processing techniques to extract valuable physiological information from facial video data captured by a standard web camera. By analyzing subtle color variations associated with blood flow, the system provides continuous and instantaneous estimations of the user's heartbeat rate. The proposed method involves the application of advanced signal processing algorithms, specifically utilizing photoplethysmography (PPG) principles, to the facial video stream. These algorithms track changes in pixel intensity over time, correlating with variations in blood volume during each cardiac cycle. To enhance accuracy and adaptability, machine learning models are employed, allowing the system to account for individual differences in skin tones, lighting conditions, and facial features. The result is a real-time analysis that enables continuous and unobtrusive monitoring of the user's heart rate. A notable advantage of this approach lies in its accessibility and cost-effectiveness, as it leverages widely available web cameras without requiring specialized hardware. The proposed system's robustness is evaluated across diverse scenarios, including varying lighting conditions and demographic groups, ensuring its reliability in real-world applications. This real-time heartbeat rate estimation using a web camera holds promise for a range of applications, from personalized health tracking to telehealth, marking a significant advancement in accessible cardiovascular monitoring