A Smart Real-Time Human Respiratory Monitoring System Based on a High-Performance Flow Sensor and an Accurate Breathing Rate Recognition Algorithm

This paper describes a real-time human respiratory monitoring system based on high-performance flow sensors, which incorporates a high-accuracy algorithm for extracting respiratory rate and monitoring abnormal respiration. The experimental findings demonstrate that the maximum absolute error of the frequency extraction algorithm is 0.007 Hz, and the average absolute error is 0.0003 Hz, which is superior by one order of magnitude to previous work. Additionally, we have developed an abnormal respiration algorithm that enables the recognition of specific breathing symptoms, such as apnea, bradypnea, tachypnea, hyperpnea, and hypopnea. Our approach involved integrating sensors, circuits, algorithms, and software to develop a portable smart human respiratory monitoring system that can share respiration information and conclusions through a smartphone application. The accuracy of the respiration monitoring results of this system indicates its potential utility in point-of-care settings. This work is a step towards the development of low-cost and high-performance respiratory monitoring systems that can improve early detection and management of respiratory disorders.