Real Time Estimation of Heart Rate under Lighting of Smartphone Camera

This paper presents an innovative approach for real-time heart rate estimation using a smartphone camera, addressing the challenges posed by fluctuating lighting conditions and motion artifacts. By merging sophisticated image processing techniques with machine learning algorithms, the system effectively extracts the fundamental photoplethysmogram (PPG) signal. PPG is a well-established, non- invasive optical method that assesses fluctuations in light absorption caused by variations in blood volume within tissues, with applications that extend beyond merely monitoring heart rate and oxygen saturation. The suggested approach enhances measurement precision and reliability by integrating robust signal processing methods with video frame analysis of the user's face, thereby ensuring consistent performance in everyday, uncontrolled settings. As PPG becomes increasingly significant in clinical and wearable health technologies, this research aids in broadening its applicability for mobile health monitoring solutions. Experimental validation demonstrates that the smartphone-based system achieves accuracy on par with traditional medical devices while providing improved portability, cost-effectiveness, and user-friendliness. The results underscore the potential of smartphone-integrated health monitoring systems as viable, scalable options for personal and remote healthcare management.