Respiratory rate estimation on embedded system

Abstract

We present the design, implementation, and results of an algorithm for respiratory rate estimation using respiratory-induced frequency, intensity, and amplitude variation calculated from the infrared (IR) channel of the SEN-15219 board for photoplethysmography (PPG) acquisition. First, the algorithm was developed in Python (on a PC) using synthetic signals and publicly available respiration and PPG data. We also include a graphical user interface to process data from sensors and display vital signs. Later, we ported the algorithm to an MSP432P401R microcontroller to complete our wearable prototype. Results are promissory and show that respiratory rate estimation can be performed on the selected platform with our proposed Fourier Product (FP) method, which results in a Mean Absolute Error of 4.1 using 16-seconds windows of IR-PPG signals.