Comprehensive analysis of pose estimation and vital sign monitoring systems

Abstract

This report provides a comprehensive analysis of modern techniques for contactless health mon- itoring, focusing on the integration of real-time pose estimation and physiological vital sign measurement. It begins by evaluating existing systems for personalized fitness and elderly care, identifying their methodological strengths and limitations. Subsequently, advanced computer vision models and machine learning algorithms for enhancing pose analysis in complex activities like Surya Namaskar and squats are explored. The report details robust camera-based methods for estimating blood pressure and respiration rate, building upon remote photoplethysmography (rPPG). The significance of clinical datasets like MIMIC-III for validating these non-invasive technologies is discussed. Finally, a review of key research papers highlights the synergistic po- tential of combining pose analysis with vital sign monitoring to create holistic, real-time health assessment tools.