PulseVision: A Real-Time Heart-Rate Mornitoring System Using Computer Vision and Signal Processing Techniques

In the dynamic landscape of technology and healthcare, the quest for real-time physiological monitoring solutions has sparked innovation. This study proposes a method that leverages computer vision and signal processing to create a cutting-edge system for monitoring heart rates in real-time. The script employs OpenCV for face detection and incorporates custom modules for real-time plotting, offering a comprehensive and instantaneous assessment of cardiovascular activity. The Python script unveils a real-time heart rate monitoring system that harnesses the synergy of computer vision and signal processing. Utilizing OpenCV for precise face detection and custom modules for dynamic plotting, the script processes video frames from a webcam to analyze the facial region for heart rate monitoring. Employing color magnification, Gaussian pyramid construction, and bandpass filtering, the script extracts the pulse signal's frequency content. Heart rate, calculated in beats per minute (BPM), provides a valuable metric for physiological assessment. This system, with its innovative approach, has the potential to redefine real- time physiological monitoring applications, offering insights for healthcare and personal well-being. Key Words: Heart Rate Monitoring, Computer Vision, Signal Processing, OpenCV, Real-time Plotting, Face Detection, Frequency Analysis, Pulse Signal, Gaussian Pyramid, Bandpass Filtering.