Monitoring Physiological and Mental Well-being through Video-Based Vital Parameter Measurement: A Review

Abstract

This study explores the possibilities of video-based vital parameter monitoring as an all-encompassing instrument for tracking mental and physical health. We apply cutting-edge computer vision and machine learning techniques to search for a continuous and non-intrusive health assessment methodology. Our method evaluates mental health indicators in addition to tracking and estimating vital metrics like heart rate and breathing rate through video data analysis. Modern technology combined with real-time monitoring eliminates the need for wearing sensors, making evaluating one's health easier and more comfortable. The research delves into creating and verifying algorithms that can precisely extract mental and physiological health data from videos. We evaluate the correctness and dependability of the suggested video-based monitoring system through thorough testing. This research has promise for early detection of health anomalies and individualized health interventions across various sectors, including telemedicine, healthcare, and well-being tracking. Additionally, because video-based monitoring is non-intrusive, it improves user compliance and comfort while mitigating certain drawbacks of conventional health monitoring techniques.