Impulse Radio Ultra-wideband Radar Research Articles

Noncontact Sensors for Vital Signs Measurement: A Narrative Review.

Vital signs are crucial for monitoring changes in patient health status. This review compared the performance of noncontact sensors with traditional methods for measuring vital signs and investigated the clinical feasibility of noncontact sensors for medical use. We searched the Medical Literature Analysis and Retrieval System Online (MEDLINE) database for articles published through September 30, 2023, and used the key search terms "vital sign," "monitoring," and "sensor" to identify relevant articles. We included studies that measured vital signs using traditional methods and noncontact sensors and excluded articles not written in English, case reports, reviews, and conference presentations. In total, 129 studies were identified, and eligible articles were selected based on their titles, abstracts, and full texts. Three articles were finally included in the review, and the types of noncontact sensors used in each selected study were an impulse radio ultrawideband radar, a microbend fiber-optic sensor, and a mat-type air pressure sensor. Participants included neonates in the neonatal intensive care unit, patients with sleep apnea, and patients with coronavirus disease. Their heart rate, respiratory rate, blood pressure, body temperature, and arterial oxygen saturation were measured. Studies have demonstrated that the performance of noncontact sensors is comparable to that of traditional methods of vital signs measurement. Noncontact sensors have the potential to alleviate concerns related to skin disorders associated with traditional skin-contact vital signs measurement methods, reduce the workload for healthcare providers, and enhance patient comfort. This article reviews the medical use of noncontact sensors for measuring vital signs and aimed to determine their potential clinical applicability.

People counting using IR-UWB radar sensors and machine learning techniques

This study aims to detect and count people using impulse radio ultra-wideband radar and machine learning algorithms. However, the data quality, difficulty distinguishing human signals from noise and clutter, and instances where human presence is not detected make it challenging to count multiple humans. To overcome these challenges, we apply wavelet transformation to reduce signal size and use simple moving averages to eliminate noise. Next, we create features based on statistical and entropic properties of the signal and apply several classification algorithms, including ANN, Random Forest, KNN, XGBOOST, and multiple linear regression, to predict the number of people present. Our findings reveal that using the ANN classifier with the Daubechies 4 (db4) wavelet provides better results than other classifiers, with an accuracy rate of 99%. Additionally, filtering the data improves accuracy, and labeling the data after extracting essential characteristics significantly improves the model’s accuracy.

基于超宽带冲激雷达的人体动作识别和心率估计数据集

Human action recognition and heart rate estimation for indoor human targets carry significant applications in many fields such as smart homes, health care, sports analysis, etc., and have attracted increasing attention in recent years. Impulse-Radio Ultra-Wideband (IR-UWB) radar, with its advantages in privacy protection, environmental requirements and performance, has found extensive applications in indoor human target monitoring. In view of the data demand for indoor human perception related research based on IR-UWB radar, and the issues with a lack of relevant public data and inadequacy in the authenticity of simulation data, this paper presents a dataset of human action recognition and heart rate estimation based on IR-UWB radar through typical indoor scene measurement experiments and data processing and annotation. The dataset collects 1,629 groups of action recognition data (i.e. unmanned scene data and 10 common actions such as walking, waving, squatting, etc.) from 10 human targets as well as 4,670 groups of heart rate estimation data. All data are presented in a standardized format with precise labeling, making it a valuable resource for research in relevant fields.

Unobtrusive hand gesture recognition using ultra-wide band radar and deep learning

<span lang="EN-US">Hand function after stroke injuries is not regained rapidly and requires physical rehabilitation for at least 6 months. Due to the heavy burden on the healthcare system, assisted rehabilitation is prescribed for a limited time, whereas so-called home rehabilitation is offered. It is therefore essential to develop robust solutions that facilitate monitoring while preserving the privacy of patients in a home-based setting. To meet these expectations, an unobtrusive solution based on radar sensing and deep learning is proposed. The multi-input multi-output convolutional eXtra trees (MIMO-CxT) is a new deep hybrid model used for hand gesture recognition (HGR) with impulse-radio ultra-wide band (IR-UWB) radars. It consists of a lightweight architecture based on a multi-input convolutional neural network (CNN) used in a hybrid configuration with extremely randomized trees (ETs). The model takes data from multiple sensors as input and processes them separately. The outputs of the CNN branches are concatenated before the prediction is made by the ETs. Moreover, the model uses depthwise separable convolution layers, which reduce computational cost and learning time while maintaining high performance. The model is evaluated on a publicly available dataset of gestures collected by three IR-UWB radars and achieved an average accuracy of 98.86%.</span>

An Excess Kurtosis People Counting System Based on 1DCNN-LSTM Using Impulse Radio Ultra-Wide Band Radar Signals

As the Artificial Intelligence of Things (AIOT) and ubiquitous sensing technologies have been leaping forward, numerous scholars have placed a greater focus on the use of Impulse Radio Ultra-Wide Band (IR-UWB) radar signals for Region of Interest (ROI) population estimation. To address the problem concerning the fact that existing algorithms or models cannot accurately detect the number of people counted in ROI from low signal-to-noise ratio (SNR) received signals, an effective 1DCNN-LSTM model was proposed in this study to accurately detect the number of targets even in low-SNR environments with considerable people. First, human-induced excess kurtosis was detected by setting a threshold using the optimized CLEAN algorithm. Next, the preprocessed IR-UWB radar signal pulses were bundled into frames, and the resulting peaks were grouped to develop feature vectors. Subsequently, the sample set was trained based on the 1DCNN-LSTM algorithm neural network structure. In this study, the IR-UWB radar signal data were acquired from different real environments with different numbers of subjects (0–10). As indicated by the experimental results, the average accuracy of the proposed 1DCNN-LSTM model for the recognition of people counting reached 86.66% at ROI. In general, a high-accuracy, low-complexity, and high-robustness solution in IR-UWB radar people counting was presented in this study.

Scheme design of the optimal background clutter suppression for impulse radio ultrawide band radar in complex fire environment: a simulation and experimental study

Impulse radio ultrawide band (IR-UWB) radar possesses several advantages, such as heat flow insensitivity, high resolution, great penetration, good mobility, and lightweight, making it a promising technology for detecting and rescuing individuals trapped in fire scenarios. However, reliable detection and extraction of vital signs of trapped people in the presence of complex fire environment, such as water droplets, smoke, wall occlusions, and reflections, require an optimal background clutter suppression scheme designed specifically for fire scenarios. This study comprehensively evaluates 20 background noise suppression schemes using an exhaustive approach that involves constructing radar echo simulation signals and designing an evaluation method for algorithms. The practical applicability of the improved design solutions was evaluated by conducting small-scale fire experiments in an acrylic chamber. The results indicate that the designed scheme is effective in overcoming the background interference from the combustion environment and is capable of supporting subsequent trajectory identification and vital sign extraction in the fire scene environment.

Convolutional Neural Networks for the Real-Time Monitoring of Vital Signs Based on Impulse Radio Ultrawide-Band Radar during Sleep.

Vital signs provide important biometric information for managing health and disease, and it is important to monitor them for a long time in a daily home environment. To this end, we developed and evaluated a deep learning framework that estimates the respiration rate (RR) and heart rate (HR) in real time from long-term data measured during sleep using a contactless impulse radio ultrawide-band (IR-UWB) radar. The clutter is removed from the measured radar signal, and the position of the subject is detected using the standard deviation of each radar signal channel. The 1D signal of the selected UWB channel index and the 2D signal applied with the continuous wavelet transform are entered as inputs into the convolutional neural-network-based model that then estimates RR and HR. From 30 recordings measured during night-time sleep, 10 were used for training, 5 for validation, and 15 for testing. The average mean absolute errors for RR and HR were 2.67 and 4.78, respectively. The performance of the proposed model was confirmed for long-term data, including static and dynamic conditions, and it is expected to be used for health management through vital-sign monitoring in the home environment.

A Study on 3D Human Pose Estimation Using Through-Wall IR-UWB Radar and Transformer

In this paper, we propose a human pose estimation algorithm for an impulse radio ultra-wideband (IR-UWB) radar based on the transformer-based deep learning model. We have built an IR-UWB radar system with an 8-by-8 multiple-input multiple-output (MIMO) antenna array. The IR-UWB radar system in our paper is advantageous for the through-wall detection application since it operates on a very low frequency range (i.e., 0.45 to 3.55 GHz) compared to other existing works on RF-based human pose estimation. Moreover, the human pose estimation by an IR-UWB radar has not been studied very well in other existing works since all existing works have used a frequency-modulated continuous wave (FMCW) radar or a WiFi device. We propose a 3D-TransPOSE algorithm for the 3D human pose estimation from the IR-UWB radar signals. The proposed algorithm is designed based on the transformer architecture. While the transformer has actively been studied in the natural language processing (NLP) or vision domains, no prior work has applied the transformer model to the RF-based human pose estimation problem. The attention mechanism of the proposed algorithm is able to focus on the relevant time segments of the IR-UWB radar signals for detecting the human pose, eliminating the needs of converting radar signals to a voxelized 3D image. We have gathered a large dataset of IR-UWB radar signals labeled with 3D human skeletons, and shown that the proposed algorithm is able to detect human skeletons with a high accuracy.

Non-contact diagnosis of sleep breathing disorders using infrared optical gas imaging: a prospective observational study

Full-night polysomnography (PSG) is the gold standard for diagnosing obstructive sleep apnea (OSA). However, PSG requires several sensors to be attached to the patient’s body, which can interfere with sleep. Moreover, non-contact devices that utilize impulse radio ultra-wideband radar have limitations as they cannot directly measure respiratory airflow. This study aimed to detect respiratory events through infrared optical gas imaging and verify its feasibility for the diagnosis of OSA. Data collection through PSG and infrared optical gas imaging was simultaneously conducted on 50 volunteers. Respiratory airflow signal was extracted from the infrared optical gas images using an automated algorithm. We compared the respiratory parameters obtained from infrared optical gas imaging with those from PSG. All respiratory events scored from the infrared optical gas imaging were strongly correlated with those identified with standard PSG sensors. Based on a receiver operating characteristic curve, infrared optical gas imaging was deemed appropriate for the diagnosis of OSA. Infrared optical gas imaging accurately detected respiratory events during sleep; therefore, it may be employed as a screening tool for OSA.

Early screening tool for developmental delay in infancy: Quantified assessment of movement asymmetry using IR-UWB radar.

In the untact COVID-19 era, the feasibility of a noncontact, impulse-radio ultrawideband (IR-UWB) radar sensor has important medical implications. Premature birth is a major risk factor for brain injury and developmental delay; therefore, early intervention is crucial for potentially achieving better developmental outcomes. Early detection and screening tests in infancy are limited to the quantification of differences between normal and spastic movements. This study investigated the quantified asymmetry in the general movements of an infant with hydrocephalus and proposes IR-UWB radar as a novel, early screening tool for developmental delay. To support this state-of-the-art technology, data from actigraphy and video camcorder recordings were adopted simultaneously to compare relevant time series as the infant grew. The data from the three different methods were highly concordant; specifically, the ρz values comparing radar and actigraphy, which served as the reference for measuring movements, showed excellent agreement, with values of 0.66 on the left and 0.56 on the right. The total amount of movement measured by radar over time increased overall; movements were almost dominant on the left at first (75.2% of total movements), but following shunt surgery, the frequency of movement on both sides was similar (54.8% of total movements). As the hydrocephalus improved, the lateralization of movement on radar began to coincide with the clinical features. These results support the important complementary role of this radar system in predicting motor disorders very early in life.

Noncontact assessment for fatigue based on heart rate variability using IR-UWB radar

Physical fatigue can be assessed using heart rate variability (HRV). We measured HRV at rest and in a fatigued state using impulse-radio ultra wideband (IR-UWB) radar in a noncontact fashion and compared the measurements with those obtained using electrocardiography (ECG) to assess the reliability and validity of the radar measurements. HRV was measured in 15 subjects using radar and ECG simultaneously before (rest for 10 min before exercise) and after a 20-min exercise session (fatigue level 1 for 0–9 min; fatigue level 2 for 10–19 min; recovery for ≥ 20 min after exercise). HRV was analysed in the frequency domain, including the low-frequency component (LF), high-frequency component (HF) and LF/HF ratio. The LF/HF ratio measured using radar highly agreed with that measured using ECG during rest (ICC = 0.807), fatigue-1 (ICC = 0.712), fatigue-2 (ICC = 0.741) and recovery (ICC = 0.764) in analyses using intraclass correlation coefficients (ICCs). The change pattern in the LH/HF ratios during the experiment was similar between radar and ECG. The subject’s body fat percentage was linearly associated with the time to recovery from physical fatigue (R2 = 0.96, p < 0.001). Our results demonstrated that fatigue and rest states can be distinguished accurately based on HRV measurements using IR-UWB radar in a noncontact fashion.

A Non-Contact Detection Method for Multi-Person Vital Signs Based on IR-UWB Radar.

With the vigorous development of ubiquitous sensing technology, an increasing number of scholars pay attention to non-contact vital signs (e.g., Respiration Rate (RR) and Heart Rate (HR)) detection for physical health. Since Impulse Radio Ultra-Wide Band (IR-UWB) technology has good characteristics, such as non-invasive, high penetration, accurate ranging, low power, and low cost, it makes the technology more suitable for non-contact vital signs detection. Therefore, a non-contact multi-human vital signs detection method based on IR-UWB radar is proposed in this paper. By using this technique, the realm of multi-target detection is opened up to even more targets for subjects than the more conventional single target. We used an optimized algorithm CIR-SS based on the channel impulse response (CIR) smoothing spline method to solve the problem that existing algorithms cannot effectively separate and extract respiratory and heartbeat signals. Also in our study, the effectiveness of the algorithm was analyzed using the Bland–Altman consistency analysis statistical method with the algorithm’s respiratory and heart rate estimation errors of 5.14% and 4.87%, respectively, indicating a high accuracy and precision. The experimental results showed that our proposed method provides a highly accurate, easy-to-implement, and highly robust solution in the field of non-contact multi-person vital signs detection.

A Portable Traffic Counting, Speed Estimation, and Classification Terminal Using IR-UWB Radar

Because of the stable performance under any light and weather conditions, radar becomes one of the most important sensors for traffic flow surveillance in intelligent transportation system (ITS). This paper proposes a novel portable traffic counting, speed estimation, and classification terminal using impulse radio ultra-wideband (IR-UWB) radar. The proposed terminal is lightweight, installed at the roadside, and potable. Moreover, it can only operate on a micro control unit of ARM Cortex-M7 with low-complexity signal processing approaches. Specifically, a vehicle detection and counting approach based on target location combined with trajectory tracking, a non-coherent speed estimation approach by exploiting the trajectory and prior geometric information, and a vehicle type classification approach based on four different attribute features are developed. Finally, experiment results show the improved performance of the proposed approaches.

Preclinical trial of noncontact anthropometric measurement using IR-UWB radar

Anthropometric profiles are important indices for assessing medical conditions, including malnutrition, obesity, and growth disorders. Noncontact methods for estimating those parameters could have considerable value in many practical situations, such as the assessment of young, uncooperative infants or children and the prevention of infectious disease transmission. The purpose of this study was to investigate the feasibility of obtaining noncontact anthropometric measurements using the impulse-radio ultrawideband (IR-UWB) radar sensor technique. A total of 45 healthy adults were enrolled, and a convolutional neural network (CNN) algorithm was implemented to analyze data extracted from IR-UWB radar. The differences (root-mean-square error, RMSE) between values from the radar and bioelectrical impedance analysis (BIA) as a reference in the measurement of height, weight, and body mass index (BMI) were 2.78, 5.31, and 2.25, respectively; predicted data from the radar highly agreed with those from the BIA. The intraclass correlation coefficients (ICCs) were 0.93, 0.94, and 0.83. In conclusion, IR-UWB radar can provide accurate estimates of anthropometric parameters in a noncontact manner; this study is the first to support the radar sensor as an applicable method in clinical situations.

Spatial-Temporal-Circulated GLCM and Physiological Features for In-Vehicle People Sensing Based on IR-UWB Radar

In-vehicle people sensing has received considerable attention for preventing overloading and forgotten children. Impulse radio ultrawideband (IR-UWB) radar is widely applied in noncontact sensing applications. However, metal-rich environment and narrow space with multiple objects bring dense multipaths in received radar signals, posing a great challenge to in-vehicle sensing, especially people counting and motion recognition. This article proposes novel spatial-temporal-circulated gray level co-occurrence matrix (STC-GLCM) and physiological features for people sensing, to detect the presence, number, and motions of people in the vehicle. Considering temporal consistency and spatial continuity of highly associated in-vehicle radar signals, STC-GLCM feature is designed to describe the 2-D circular spatial-temporal distribution, and extracted at individual, associated, and global scales. In addition, the singular value ratio (SVR) is introduced to select the physiological feature at each individual scale, representing the possibility and intensity of cardiopulmonary activity. Three different classifiers are applied on these combined features for classification, and the stochastic gradient descent (SGD) is adopted for performance analysis. A radar signal dataset is constructed for various in-vehicle scenarios, including different number of people in a stationary and moving vehicle with several motions. The accuracies reach 97.5% and 97.0% in counting the number of people in the stationary and moving vehicle, and 99.0% in recognizing the motions, demonstrating the effectiveness and robustness of these proposed features. The proposed in-vehicle radar signal dataset is available at <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><uri>https://github.com/yangxiuzhu777/In-Vehicle-Radar-Data</uri></i> .

Learning-Refined Integral Null Space Pursuit Algorithm for Noncontact Multisubjects Vital Signs Measurements Using SFCW-UWB and IR-UWB Radar

Under low signal-to-noise-and-clutter ratio circumstances, non-contact multi-subjects vital sign assessment based on ultrawideband (UWB) radar is extremely difficult. It is challenging to obtain an accurate measurement since the respiratory frequency harmonics are mixed with noise during detection and are extremely near to the frequency of the heartbeat signal. We use an impulse radio ultrawideband (IR-UWB) radar and a stepped-frequency continuous-wave ultrawideband (SFCW-UWB) radar in measurement to get reliable vital signs from several participants. For adaptively extracting respiratory and heartbeat patterns, it is proposed to use the learning refined integral null space pursuit algorithm (LR-INSP), which is based on the learning refined integral null space pursuit method and optimum route learning. The refined-INSP technique aims to provide fine-grained multi-layer and multi-branch harmonic components. Then, to obtain a reliable and stable vital sign path from the multi-branch decomposition, an optimum path learning technique is presented. According to the experimental findings, the average signal accuracy assessment (SAA) of the heartbeat and respiratory frequencies for IR-UWB radar is 95.41% and 89.97%, respectively. The average SAA of the heartbeat and respiratory frequencies for SFCW-UWB radar is 94.39% and 99.48%, respectively.

Non-contact Sleep/Wake Monitoring Using Impulse-Radio Ultrawideband Radar in Neonates.

Background: The gold standard for sleep monitoring, polysomnography (PSG), is too obtrusive and limited for practical use with tiny infants or in neonatal intensive care unit (NICU) settings. The ability of impulse-radio ultrawideband (IR-UWB) radar, a non-contact sensing technology, to assess vital signs and fine movement asymmetry in neonates was recently demonstrated. The purpose of this study was to investigate the possibility of quantitatively distinguishing and measuring sleep/wake states in neonates using IR-UWB radar and to compare its accuracy with behavioral observation-based sleep/wake analyses using video recordings.Methods: One preterm and three term neonates in the NICU were enrolled, and voluntary movements and vital signs were measured by radar at ages ranging from 2 to 27 days. Data from a video camcorder, amplitude-integrated electroencephalography (aEEG), and actigraphy were simultaneously recorded for reference. Radar signals were processed using a sleep/wake decision algorithm integrated with breathing signals and movement features.Results: The average recording time for the analysis was 13.0 (7.0–20.5) h across neonates. Compared with video analyses, the sleep/wake decision algorithm for neonates correctly classified 72.2% of sleep epochs and 80.6% of wake epochs and achieved a final Cohen's kappa coefficient of 0.49 (0.41–0.59) and an overall accuracy of 75.2%.Conclusions: IR-UWB radar can provide considerable accuracy regarding sleep/wake decisions in neonates, and although current performance is not yet sufficient, this study demonstrated the feasibility of its possible use in the NICU for the first time. This unobtrusive, non-contact radar technology is a promising method for monitoring sleep/wake states with vital signs in neonates.

Respiration Based Non-Invasive Approach for Emotion Recognition Using Impulse Radio Ultra Wide Band Radar and Machine Learning.

Emotion recognition gained increasingly prominent attraction from a multitude of fields recently due to their wide use in human-computer interaction interface, therapy, and advanced robotics, etc. Human speech, gestures, facial expressions, and physiological signals can be used to recognize different emotions. Despite the discriminating properties to recognize emotions, the first three methods have been regarded as ineffective as the probability of human’s voluntary and involuntary concealing the real emotions can not be ignored. Physiological signals, on the other hand, are capable of providing more objective, and reliable emotion recognition. Based on physiological signals, several methods have been introduced for emotion recognition, yet, predominantly such approaches are invasive involving the placement of on-body sensors. The efficacy and accuracy of these approaches are hindered by the sensor malfunctioning and erroneous data due to human limbs movement. This study presents a non-invasive approach where machine learning complements the impulse radio ultra-wideband (IR-UWB) signals for emotion recognition. First, the feasibility of using IR-UWB for emotion recognition is analyzed followed by determining the state of emotions into happiness, disgust, and fear. These emotions are triggered using carefully selected video clips to human subjects involving both males and females. The convincing evidence that different breathing patterns are linked with different emotions has been leveraged to discriminate between different emotions. Chest movement of thirty-five subjects is obtained using IR-UWB radar while watching the video clips in solitude. Extensive signal processing is applied to the obtained chest movement signals to estimate respiration rate per minute (RPM). The RPM estimated by the algorithm is validated by repeated measurements by a commercially available Pulse Oximeter. A dataset is maintained comprising gender, RPM, age, and associated emotions which are further used with several machine learning algorithms for automatic recognition of human emotions. Experiments reveal that IR-UWB possesses the potential to differentiate between different human emotions with a decent accuracy of 76% without placing any on-body sensors. Separate analysis for male and female participants reveals that males experience high arousal for happiness while females experience intense fear emotions. For disgust emotion, no large difference is found for male and female participants. To the best of the authors’ knowledge, this study presents the first non-invasive approach using the IR-UWB radar for emotion recognition.

Preclinical evaluation of noncontact vital signs monitoring using real-time IR-UWB radar and factors affecting its accuracy

Recently, noncontact vital sign monitors have attracted attention because of issues related to the transmission of contagious diseases. We developed a real-time vital sign monitor using impulse-radio ultrawideband (IR-UWB) radar with embedded processors and software; we then evaluated its accuracy in measuring heart rate (HR) and respiratory rate (RR) and investigated the factors affecting the accuracy of the radar-based measurements. In 50 patients visiting a cardiology clinic, HR and RR were measured using IR-UWB radar simultaneously with electrocardiography and capnometry. All patients underwent HR and RR measurements in 2 postures—supine and sitting—for 2 min each. There was a high agreement between the RR measured using radar and capnometry (concordance correlation coefficient [CCC] 0.925 [0.919–0.926]; upper and lower limits of agreement [LOA], − 2.21 and 3.90 breaths/min). The HR measured using radar was also in close agreement with the value measured using electrocardiography (CCC 0.749 [0.738–0.760]; upper and lower LOA, − 12.78 and 15.04 beats/min). Linear mixed effect models showed that the sitting position and an HR < 70 bpm were associated with an increase in the absolute biases of the HR, whereas the sitting position and an RR < 18 breaths/min were associated with an increase in the absolute biases of the RR. The IR-UWB radar sensor with embedded processors and software can measure the RR and HR in real time with high precision. The sitting position and a low RR or HR were associated with the accuracy of RR and HR measurement, respectively, using IR-UWB radar.

Non-contact screening system based for COVID-19 on XGBoost and logistic regression

BackgroundThe coronavirus disease (COVID-19) effected a global health crisis in 2019, 2020, and beyond. Currently, methods such as temperature detection, clinical manifestations, and nucleic acid testing are used to comprehensively determine whether patients are infected with the severe acute respiratory syndrome coronavirus 2. However, during the peak period of COVID-19 outbreaks and in underdeveloped regions, medical staff and high-tech detection equipment were limited, resulting in the continued spread of the disease. Thus, a more portable, cost-effective, and automated auxiliary screening method is necessary. ObjectiveWe aim to apply a machine learning algorithm and non-contact monitoring system to automatically screen potential COVID-19 patients. MethodsWe used impulse-radio ultra-wideband radar to detect respiration, heart rate, body movement, sleep quality, and various other physiological indicators. We collected 140 radar monitoring data from 23 COVID-19 patients in Wuhan Tongji Hospital and compared them with 144 radar monitoring data from healthy controls. Then, the XGBoost and logistic regression (XGBoost + LR) algorithms were used to classify the data according to patients and healthy subjects. ResultsThe XGBoost + LR algorithm demonstrated excellent discrimination (precision = 92.5%, recall rate = 96.8%, AUC = 98.0%), outperforming other single machine learning algorithms. Furthermore, the SHAP value indicates that the number of apneas during REM, mean heart rate, and some sleep parameters are important features for classification. ConclusionThe XGBoost + LR-based screening system can accurately predict COVID-19 patients and can be applied in hotels, nursing homes, wards, and other crowded locations to effectively help medical staff.