Abstract
During the pandemic of coronavirus disease-2019 (COVID-19), medical practitioners need non-contact devices to reduce the risk of spreading the virus. People with COVID-19 usually experience fever and have difficulty breathing. Unsupervised care to patients with respiratory problems will be the main reason for the rising death rate. Periodic linearly increasing frequency chirp, known as frequency-modulated continuous wave (FMCW), is one of the radar technologies with a low-power operation and high-resolution detection which can detect any tiny movement. In this study, we use FMCW to develop a non-contact medical device that monitors and classifies the breathing pattern in real time. Patients with a breathing disorder have an unusual breathing characteristic that cannot be represented using the breathing rate. Thus, we created an Xtreme Gradient Boosting (XGBoost) classification model and adopted Mel-frequency cepstral coefficient (MFCC) feature extraction to classify the breathing pattern behavior. XGBoost is an ensemble machine-learning technique with a fast execution time and good scalability for predictions. In this study, MFCC feature extraction assists machine learning in extracting the features of the breathing signal. Based on the results, the system obtained an acceptable accuracy. Thus, our proposed system could potentially be used to detect and monitor the presence of respiratory problems in patients with COVID-19, asthma, etc.
Highlights
Based on the background mentioned earlier, we propose a non-contact breathing pattern detection using frequency-modulated continuous wave (FMCW) radar with Xtreme Gradient Boosting (XGBoost) classifier and Mel-frequency cepstral coefficient (MFCC) feature extraction in an indoor environment
Based on the experiments above, we showed that adding MFCC feature extraction gives a better result than without and with statistical feature extraction
We have proposed a non-contact monitoring and classification system for breathing patterns using XGBoost classifier and MFCC feature extraction
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. On 30 January 2020, the World Health Organization (WHO) officially confirmed that the spread of COVID-19 had caused a global pandemic for countries around the world [1,2]. Machine-learning assistance in classifying the breathing pattern plays an important role in detecting respiratory disorder. We tried to incorporate radar technology with machine learning to build a system that can detect and classify the breathing pattern disorder. Based on the background mentioned earlier, we propose a non-contact breathing pattern detection using FMCW radar with XGBoost classifier and MFCC feature extraction in an indoor environment. Some signal processing steps are implemented to extract the breathing information from chest displacement information. The implementation of the proposed system was tested for a real-time operation and successfully detected five different classes of breathing waveform. The rest of the chapter is summarized as follows: Section 2 describes the related work, Section 3 explains the proposed method, Section 4 demonstrates the experimental result, and Section 5 concludes the work
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
