DEVELOPMENT OF A SYSTEM FOR DETERMINING THE FREQUENCY OF SURFACE RESPIRATION ACCORDING TO SMARTPHONE BIOMETRIC SENSORS

Abstract

Modern realities set digitalization tasks for humanity in various areas of work and life, speeding up the deadline for completing tasks and facilitating them. Modern technology equipped with sensors that can be used for preliminary diagnosis allows you to identify various symptoms that may be the reason for visiting medical institutions. This allows you to gain time – a very valuable resource when it comes to human life. Therefore, the ability to perform such diagnostics the determination of respiratory rate, is an urgent task today. The article presents a method for determining the respiratory rate using a three-axis accelerometer on a mobile device. This method can be used in a user health monitoring application in the absence of smart watches. The method allows the user to measure the respiratory rate of a person only if the user is in a sitting position and a mobile device equipped with the necessary sensor is in the upper anterior thigh area (pocket area). The algorithm for determining the respiratory rate is implemented in two programming languages: Python and MatLab. The algorithm uses a respiratory rate stabilizer, because the accelerometer sampling rate is not constant from an Android-based mobile device. Next, the signal is normalized by the z-normalization method. To isolate the frequency interval in which the respiratory rate is calculated, the Butterworth filter of the 1st order is used. The analysis of independent components makes it possible to obtain its independent components from a mixture of signals. Several implementations of this method have been tested in Python and Matlab. The best quality results were shown by an algorithm implemented in MatLab using the built-in reconstructive analysis of independent components (RICA) from a set of statistics and machine learning tools. In terms of speed, the best results were shown by the implementation of the algorithm in Python with the method of fast analysis of independent components (FastICA). The MSE for the range of 10-20 breaths per minute was 2.14 breaths per minute. The MSE for 20-30 breaths per minute was 3.46 breaths per minute.