Published Paper Details:

During the Covid-19 era, everyone grasped the significance of non-contact health monitoring technology. Despite adhering to social distance rules, it is unacceptable to use the same gadget to track each patient's HR because each patient has a unique health condition. We then came up with the notion of measuring Pulse rate using a webcam and image processing to resolve all these concerns. We attempted to create such a system from scratch using the Python programming language, which helped us comprehend the area as well as its benefits and drawbacks in comparison to other biometric authentication techniques. After conducting research, we chose to utilize the OpenCV library for Python and Face API for face recognition because the project's requirements precluded us from utilizing a system that could consistently perform both detection and recognition. The human body's cardiovascular system inspired the concept of passive physiological parameter monitoring. Because of the circulatory system, the heart can continuously pump blood throughout the body. When our heart pumps blood through every blood artery in our body, the color of the skin on our faces varies with each heartbeat. Therefore, it is possible to determine the HR from the color fluctuation of facial skin. Heartbeat, i.e., cardiac pulse measurement is one of the most frequent examinations performed in health care monitoring. Electrocardiography (ECG) and Photoplethysmography (PPG) are the procedures currently utilized in mainstream medicine to monitor heartbeats. Heart rate is the frequency of a heartbeat over a given period. Every person's heart rate is different depending on their circumstances, age, and time. Monitoring the variations in heart rate can be used to measure physiological characteristics, emotional arousal, and cardiac issues. The different heart rate measuring techniques are categorized as follows: (1) Contact 2) Non-contact. Contact approaches are the ones that are most frequently employed for heart rate extraction. This technique employs several sensors that come into direct contact with the human body. ECG sensors, pulse oximeter sensors, and other similar devices are some examples of contact-based heart rate monitoring systems. Optical sensors are used in non-contact procedures, which do not involve placing anything on the human body. Non-contact technologies like laser, radar, and microwave doppler require highly precise sensors. Laser light and radar signals have certain detrimental effects on human health. One of the greatest methods is HR monitoring without any physical contact. Computer algorithms are used to process images and increase the accuracy of digital photos and movies. It is possible to use a considerably wider variety of algorithms to handle the incoming data without experiencing issues like signal distortion and noise accumulation. Evaluation is made simpler and more affordable by being able to determine Heart Rate variability from a simple recorded facial film. With our strategy, we present a fresh way to solve all these issues. This innovative approach, which is based on automatic face tracking and blind source separation of color channels into separate independent components, can be used on color video recordings of the human face. We were able to achieve high accuracy and correlation even in the presence of movement artefacts and various lighting conditions when we compared the heart pulse rate derived from the color films captured by a very basic camera to a pulse oximeter and fitness band.