Using Machine Learning Methods for Contactless Monitoring of Abnormality in Breathing Patterns

Abstract

The objective of the present work was to develop and evaluate different methods for measuring and detecting abnormalities in breathing patterns using a pulsed coherent radar sensor. This study represents a crucial initial step toward developing a life-saving hardware product. The contactless monitoring of breathing abnormalities offers significant advantages over traditional methods using contact sensors, particularly in medical scenarios such as post-stroke recovery. Experiments were conducted at three different distances (0.35 − 0.45m, 0.75 − 0.90m, and 1.20 − 1.40m) using a pulsed coherent radar system. Machine learning methods, including kNN (k-Nearest Neighbors) and ANN-MLP (Artificial Neural Networks Multilayer Perceptron), were employed to distinguish whether the monitored individual exhibited normal or abnormal breathing patterns. Our analysis revealed that employing ML models and data signals from the radar sensor holds promise for classifying breathing pattern abnormalities. This paper covers the steps performed to accomplish this: a) Equipment and Data Processing; b) Measurement Trials and Data Collection; c) Study involving k-Nearest Neighbors method and Feed-Forward Neural Networks.