A Real Time and Remote Intelligent Breathing Rate and Breathing Pattern Monitoring System.
Author(s):
P. Suresh, A.Shivaram, P.Lavanya, V.Reshma, R.Pujith
Keywords:
Breathing pattern, Breathing rate, Portable equipment, Physiological signal processing, Non-contact technique.
Abstract
The aim of this project is to develop an affordable real time remote intelligent breathing rate monitoring system. To achieve low-cost and remote measurement of respiratory signal, and Pi camera collaborated with marker tracking is used as data acquisition sensor, and a Raspberry Pi 4 is used as data processing platform. To overcome challenges in actual applications, the signal processing algorithms are designed for removing sudden body movements and filters are used for smoothing of raw signal. Subsequently, breathing rate is estimated by a translational cross point algorithm, and breathing pattern is identified by machine learning algorithms. For estimating respiratory rate, the translational cross point algorithm performs better than other methods with RMSE of 3.29 bpm. With respect to the classification of breathing patterns, the established machine learning performs better than other classifiers with the accuracy, precision, recall, and F1 score of 92.4%, 90.3%, 91.1%, and 90.5% respectively. The obtained decision-making information containing estimated breathing rate and pattern are sent to user’s smartphone via a cloud service platform. In a way, due to its low-price, non-contact and portable merits, the system performs with high accuracy and robustness.
Article Details
Unique Paper ID: 155271

Publication Volume & Issue: Volume 9, Issue 1

Page(s): 417 - 423
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