Projects:2020s2-7451 Analysing Health Data from Wearables

Abstract

Heart rate (HR) and respiratory rate (RR) are important physiological parameters that could be used to indicate ones health and abnormalities in these parameters could be an indicator of serious illness. Therefore, many different methods have been proposed for automatic monitoring of HR and RR. In this study, we would use photoplethysmogram (PPG) signal to extract HR and RR data through development of algorithms in Matlab. Firstly, digital filtering would be used to detect and distinguish noisy signal and also motion artifact from a raw PPG signal. Then, the filtered signal would be extracted to gain HR and RR data.

Introduction

PPG is a simple optical technique used to detect the volumetric changes in blood. It is a low cost and non-invasive method that makes measurement on the surface of the skin. Different from electrocardiogram (ECG), PPG does not focus on the electrical pulses that causes heart to beat, but rather on the effect that the beating has on blood vessels. With every beat of heart, blood would be pumped out of the heart into blood vessels putting pressure on the vessel wall and causing them to dilate. These blood vessel would relax again between two heart beats. In this study, a camera-based PPG is used as it enables remote vital signal monitoring by using cameras. This would be cost efficient since cameras are available in everyday item such as smartphone. By lighting finger tip for 60 seconds with the flash light of smartphone camera, minisclue changes in the amount of blood that flows through the dilating and relaxing capillaries would be measured based on the amount of light that is reflected back to the camera. When the capillaries are dilated, meaning it contains a lot of blood, a lot of the light would be absorbed by the blood and therefore just a little would be reflected. Vice versa, when the capillaries are in relax condition, not many of the light would be absorbed and hence there would be more light reflected back to the camera.

Project team

Project students

• Amirahtul Nazihah Amir
• Bin Dai
• Jiaping Wang

Project supervisors

• Associate Professor Mathias Baumert
• Associate Professor Brian Ng