Abstract Body (Do not enter title and authors here): Introduction
In recent years, the development of wearable devices and bioinformation analysis applications utilizing smartphone sensors has seen significant advancements. Despite these technological strides, their adoption and sustained use remain limited to health-conscious individuals. The general populace, particularly those indifferent to health management, stands to miss out on the potential benefits of wearable technologies due to a lack of engagement. This disconnect underscores the necessity for more accessible health monitoring solutions.

Research Questions and Goals
This study aims to bridge the gap in health monitoring accessibility by introducing a system capable of non-invasive, non-contact acquisition of biometric information. Such a system seeks to democratize the early detection and prevention of diseases across various demographics.

Methods and Results
A prospective cohort clinical study was conducted in a hospital setting with approximately 200 consenting patients and healthy controls. Images of the subjects' faces and hands were captured with a high-speed camera, and blood pressure measurements were taken simultaneously for correlation.
A machine learning algorithm was developed to analyze skin blood flow and spectral features from these images to identify potential diseases. The study specifically focused on early detection of hypertension and diabetes. Hypertension was categorized for individuals with systolic blood pressure ≥115 mmHg or diastolic blood pressure ≥75 mmHg. Diabetes was identified in subjects with HbA1c levels ≥6.5 or those previously diagnosed.
The algorithm's performance in detecting hypertension showed a 94.2% in alignment with the AHA guidelines for stage1 hypertension, based on pulse wave features. For a subset including 40% early hypertension cases, accuracy rates were 86.2% for 30-second data segments and 80.9% for 5-second segments.
For diabetes detection, the application of the algorithm to video data achieved a 75.3%, utilizing blood flow patterns as markers. Hypertension and diabetes were analyzed using different features.

Conclusions
The integration of AI algorithms with video data analysis of skin and blood vessels demonstrates a promising avenue for the non-contact, early detection of hypertension and diabetes. This approach not only offers a viable non-contact monitoring technology, but also represents a significant leap towards inclusive, accessible health care prevention and management strategies.