Optimizing Respiratory Assessment with Advanced Photoplethysmography Techniques

The Photoplethysmography (PPG) is becoming a more popular tool for assessing a person's physiological condition in everyday life. In this work, we present a novel and efficient method for breathing real-time detection employing PPG in conjunction with cutting-edge methods. The methodology encompasses the selection of a ROI, extraction of features, and calculation of magnitudes., OpenCV for image processing, and Gaussian blur to enhance the PPG signal. Our methodology can be used for continuous, non-intrusive monitoring in a variety of scenarios since it utilizes the webcam's capabilities. We investigated the effectiveness of our proposed method through extensive experiments on both provided dataset and real-time data. The findings indicated a high level of sensitivity and accuracy in breath detection, with few cases of false positives and false negatives. Additionally, the system is applicable in real-world applications due to its computing efficiency, which enables real-time implementation. Our suggested breathing detection method, which combines PPG with cutting-edge methodologies, has remarkable potential in a number of applications, including stress management, sleep analysis, and remote health monitoring, according to the study's findings. The contact-free nature of the method increases user comfort and convenience, thereby enhancing its practicality in healthcare and wellness applications.