Radar Range-Breathing Separation for the Automatic Detection of Humans in Cluttered Environments

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.

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Non-Contact Monitoring and Classification of Breathing Pattern for the Supervision of People Infected by COVID-19

Purnomo

Lin

Adiprabowo

et al. 2021

“…Importantly, an increase in operational frequency offers the potential to significantly increase the transmitted bandwidth to provide very high range resolution, in the order of centimetres, to provide an overall resolution capable of producing useful radar images. Modern millimetre-wave radar techniques and new sensing approaches allow us to determine the parameters of objects with high accuracy [ 5 , 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

Experimental Evaluation of 79 and 300 GHz Radar Performance in Fire Environments

Bystrov

Daniel

Hoare

et al. 2021

This paper presents an experimental study of the propagation of mm-wave/low-THz signals in the frequency ranges of 79 and 300 GHz through fire. Radar performance was investigated in various real scenarios, including fire with strong flame, dense smoke and water vapour. A stereo video camera and a LIDAR were used as a comparison with other common types of sensors. The ability of radars to enable the visibility of objects in fire environments was proven. In all scenarios, the radar signal attenuation was measured, and in the case of steam was compared with theoretical calculations. The analysis of the experimental results allows us to conclude that there are good prospects for millimetre wave and Low Terahertz radar in the field of firefighting imaging equipment.

“…Cardillo et al [ 39 ] proposed a new radar sensing approach for automatic detection and ranging of human targets in cluttered environments, without the need for any target thresholding. Their approach is robust to artefacts due to presence of multiple objects within the same radiation beam.…”

Section: Introductionmentioning

confidence: 99%

Classification of Hemodynamics Scenarios from a Public Radar Dataset Using a Deep Learning Approach

Slapničar

Wang

Luštrek

2021

Contact-free sensors offer important advantages compared to traditional wearables. Radio-frequency sensors (e.g., radars) offer the means to monitor cardiorespiratory activity of people without compromising their privacy, however, only limited information can be obtained via movement, traditionally related to heart or breathing rate. We investigated whether five complex hemodynamics scenarios (resting, apnea simulation, Valsalva maneuver, tilt up and tilt down on a tilt table) can be classified directly from publicly available contact and radar input signals in an end-to-end deep learning approach. A series of robust k-fold cross-validation evaluation experiments were conducted in which neural network architectures and hyperparameters were optimized, and different data input modalities (contact, radar and fusion) and data types (time and frequency domain) were investigated. We achieved reasonably high accuracies of 88% for contact, 83% for radar and 88% for fusion of modalities. These results are valuable in showing large potential of radar sensing even for more complex scenarios going beyond just heart and breathing rate. Such contact-free sensing can be valuable for fast privacy-preserving hospital screenings and for cases where traditional werables are impossible to use.