Artificial Intelligence and Photoplethysmography (PPG) Sensors in Smart Watches to Counter Cardiovascular Sudden Deaths

Photoplethysmography (PPG) technology, combined with artificial intelligence, is revolutionizing non-invasive health monitoring through wearable devices. This research focuses on its advanced capabilities in assessing cardiovascular health, cuffless blood pressure, sleep patterns, and diabetes management. Purpose: This study aims to investigate the expanded role of Photoplethysmography (PPG) technology in non-invasive healthcare monitoring, focusing on its potential for continuous assessment of cardiovascular health, cuffless blood pressure, sleep, and diabetes management, enhanced by artificial intelligence (AI) to improve early detection and diagnostic precision. Design / Methodology / Approach: The research employs AI techniques, including deep learning and machine learning, to analyse PPG signals from wearable devices such as smartwatches and fitness bands. Second derivative analysis is utilized to identify subtle cardiovascular indicators, while novel algorithms are developed to detect and mitigate motion artifacts, ensuring data accuracy. Findings: AI-driven PPG analysis enables accurate cuffless blood pressure measurements and early detection of heart disease by identifying markers of arterial stiffness and other cardiovascular issues. Advanced algorithms effectively reduce motion artifact errors, improving the reliability of health data collected from wearable devices. Research Limitations/Implications: Motion artifacts continue to pose challenges to PPG accuracy, particularly during physical activity. Further research is required to enhance algorithm robustness and validate findings across diverse populations and real-world conditions. Practical Implications: The integration of AI with PPG in wearable devices provides a cost-effective, non-invasive solution for continuous health monitoring, enabling early intervention for cardiovascular and metabolic conditions, which could improve patient outcomes and accessibility to preventive healthcare. Originality/Value: This study introduces a novel combination of AI-driven deep learning and second derivative analysis in PPG technology, extending its capabilities beyond traditional metrics to offer detailed insights into cardiovascular health, with significant potential for scalable, real-time health monitoring