Two Optimized IoT Device Architectures Based on Fast Fourier Transform to Monitor Patient’s Photoplethysmography and Body Temperature

Abstract

The measurement of blood-oxygen saturation (SpO2), heart rate (HR), and body temperature are very critical in monitoring patients. Photoplethysmography (PPG) is an optical method that can be used to measure heart rate, blood-oxygen saturation, and many analytics about cardiovascular health of a patient by analyzing the waveform. With the COVID-19 pandemic, there is a high demand for a product that can remotely monitor such parameters of a COVID-19 patient. This paper proposes two major design architectures for the product with optimized system implementations by utilizing the ESP32 development environment and cloud computing. In one method, it discusses edge computing with the fast Fourier transform (FFT) and valley detection algorithms to extract features from the waveform before transferring data to the cloud, and the other method transfers raw sensor values to the cloud without any loss of information. This paper especially compares the performance of both system architectures with respect to bandwidth, sampling frequency, and loss of information.