What this Trial Is About

Vital sign monitoring is important for patients, especially those with underlying cardiovascular diseases. Traditional monitoring including SaO2, blood pressure, pulse, rhythm monitoring can be cumbersome, with wires and cuff attached to a patient. This can lead to reduced compliance, especially in uncooperative patients or young kids. The use of photoplethysmography (PPG) is simple and is widely used clinically to achieve continuous monitoring of SaO2, respiratory rate and pulse rate. PPG also contains important physiological signals that can correlate with blood pressure. Remote PPG (rPPG) with can be collected by cameras. This is being developed to achieve contactless monitoring. Theoretically, by analyzing video recording, the fluctuation of intensity of lights reflected from patients' body surface can be measured. Physiological parameters such as heart rate, SaO2, and blood pressure can then be calculated. However, because of environmental signal contamination, motion artefacts and other limitations, rPPG remains inaccurate for routine clinical use. To improve accuracy of PPG recordings, machine learning approach has been attempted. This study aims to gather physiological data from patients to train these models.

Cardiovascular Physiological Signal Recording Project