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.

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The presence of an obstacle in detecting respiration vital signs using a radar system does not only exhibit attenuation and phase shift effects on the radar signal but also causes additional beat frequencies to appear beside those from the target. When a conventional FMCW radar system detects the peak of the received signal spectrum as a beat frequency, this will give an error of detection. The respiratory vital signs will then be undetectable. A method to overcome this obstacle problem is needed. Therefore the implementation of the FMCW radar system in detecting respiratory vital signs behind the wall can be realized. In this paper, a method to deal with the previously mentioned problem is proposed. The proposed method consists of a method for identifying the obstacle response. Then the results are used to eliminate the beat frequency that arises from the obstacle structure, and therefore the signal to clutter ratio (SCR) can be improved. Furthermore, the method is combined with the phase detection method, which is applied to extract the Doppler response related to the target respiratory vital signs. The weighting process by elaborating cross correlation is also employed for minimizing clutter from other objects reflection under debris. A laboratory experiment by developing an FMCW radar system with a 24 GHz frequency operation was performed in this research. The proposed detection method was elaborated in the post-processing of the radar system. The experiment result demonstrates that the proposed method is capable of eliminating the effect of the wall and successfully extracting the respiration vital sign pattern.

A respiratory disorder that attacks COVID-19 patients requires intensive supervision of medical practitioners during the isolation period. A non-contact monitoring device will be a suitable solution for reducing the spread risk of the virus while monitoring the COVID-19 patient. This study uses Frequency-Modulated Continuous Wave (FMCW) radar and Machine Learning (ML) to obtain respiratory information and analyze respiratory signals, respectively. Multiple subjects in a room can be detected simultaneously by calculating the Angle of Arrival (AoA) of the received signal and utilizing the Multiple Input Multiple Output (MIMO) of FMCW radar. Fast Fourier Transform (FFT) and some signal processing are implemented to obtain a breathing waveform. ML helps the system to analyze the respiratory signals automatically. This paper also compares the performance of several ML algorithms such as Multinomial Logistic Regression (MLR), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), eXtreme Gradient Boosting (XGB), Light Gradient Boosting Machine (LGBM), CatBoosting (CB) Classifier, Multilayer Perceptron (MLP), and three proposed stacked ensemble models, namely Stacked Ensemble Classifier (SEC), Boosting Tree-based Stacked Classifier (BTSC), and Neural Stacked Ensemble Model (NSEM) to obtain the best ML model. The results show that the NSEM algorithm achieves the best performance with 97.1% accuracy. In the real-time implementation, the system could simultaneously Manuscript

The measurement of heartbeat rate and breathing rate for patients with sensitive skin, such as skin with burns, is very difficult to do, especially if the number of patients is large and medical personnel is limited. Therefore, this study seeks to propose a preliminary solution to this problem by proposing a device that can measure the vital signs of several people concurrently, especially the heartbeat rate and breathing rate, without attaching sensors to their skin. This is done using an FMCW (frequency-modulated continuous wave) radar that operates at 77–81 GHz. FMCW radar emits electromagnetic waves towards the chest of several targets and picks up the reflected waves. Then, using signal processing of these reflected waves, each target’s heartbeat rate and breathing rate can be obtained. Our experiment managed to perform concurrent detection for four targets. The experimental results are between 52 and 82 beats per minute for the heartbeat rates and between 10 and 35 breaths per minute for the breathing rates of four targets. These results are in accordance with normal heartbeat rate and normal breathing rate; thus, our research succeeded in proposing a preliminary solution to this problem.