Impulse Radio Ultra-wideband Research Articles

Overview of ultra-wideband transceivers—system architectures and applications

The Ultra-WideBand (UWB) technique, which offers good energy efficiency, flexible data rate, and high ranging accuracy, has recently been recognized as a revived wireless technology for short distance communication. This paper presents a brief overview of two UWB techniques, covering Impulse-Radio UWB (IR-UWB) and Frequency-Modulation UWB (FM-UWB) methods. The link margin enhancement technique, Very-WideBand (VWB), and power consumption reducing technique, chirp UWB, are also introduced. Then, several potential applications of IR-UWB with transceiver architectures are addressed, including high data rate proximity communication and secure wireless connectivity. With fine-ranging and energy-efficient communication features, the UWB wireless technology is highly promising for secure mobile Internet of Things (IoT) applications.

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.

Positioning the Adjacent Buried Objects Using UWB Technology Combine with Levenberg Marquardt Algorithm

The determination of the buried objects and cracks in building structures is an important issue in real-life. In this paper, we propose a new method called Correlation Function Separation Technique (CFST) combine with the Lervenberg-Marquardt Algorithm (LMA) using the Impulse Radio Ultra-Wide Band (IR-UWB) penetrating system to improve the accuracy in detecting and positioning of the adjacent buried objects in building structures. Based on the UWB signal processing, the proposed method can be used to determine both the relative permittivity of the environment and the position of the buried objects, especially the adjacent objects. The analytical method is validated by mathematical proofs and Matlab simulations, and the position errors are used to assess the performance of proposed method. The numerical results shown that the proposed method can be used for positioning the adjacent buried objects in the homogeneous environment which has an average positioning error of 3.52cm, which is smaller than that of the conventional method based on B-canned radar images processing.

Markerless Gait Classification Employing 3D IR-UWB Physiological Motion Sensing

Human gait refers to the propulsion achieved by the effort of human limbs, a reflex progression resulting from the rhythmic reciprocal bursts of flexor and extensor activity. Several quantitative models are followed by health professionals to diagnose gait abnormality. Marker-based gait quantification is considered a gold standard by the research and health communities. It reconstructs motion in 3D and provides parameters to measure gait. But, it is an expensive and intrusive technique, limited to soft tissue artefact, prone to incorrect marker positioning, and skin sensitivity problems. Hence, markerless, swiftly deployable, non-intrusive, camera-less prototypes would be a game changing possibility, and an example is proposed here. This paper illustrates a 3D gait motion analyser employing impulse radio ultra-wide band (IR-UWB) wireless technology. The prototype can measure 3D motion and determine quantitative parameters considering anatomical reference planes. Knee angles have been calculated from the gait by applying vector algebra. Simultaneously, the model has been corroborated with the popular markerless camera based 3D motion capturing system, the Kinect sensor. Bland and Altman (B&A) statistics has been applied to the proposed prototype and Kinect sensor results to verify the measurement agreement. Finally, the proposed prototype has been incorporated with popular supervised machine learning such as, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -nearest neighbour ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$kNN$ </tex-math></inline-formula> ), support vector machine (SVM) and the deep learning technique deep neural multilayer perceptron (DMLP) network to automatically recognize gait abnormalities, with promising results presented.

A Real-Time Respiration Monitoring and Classification System Using a Depth Camera and Radars.

Respiration rate (RR) and respiration patterns (RP) are considered early indicators of physiological conditions and cardiorespiratory diseases. In this study, we addressed the problem of contactless estimation of RR and classification of RP of one person or two persons in a confined space under realistic conditions. We used three impulse radio ultrawideband (IR-UWB) radars and a 3D depth camera (Kinect) to avoid any blind spot in the room and to ensure that at least one of the radars covers the monitored subjects. This article proposes a subject localization and radar selection algorithm using a Kinect camera to allow the measurement of the respiration of multiple people placed at random locations. Several different experiments were conducted to verify the algorithms proposed in this work. The mean absolute error (MAE) between the estimated RR and reference RR of one-subject and two-subjects RR estimation are 0.61±0.53 breaths/min and 0.68±0.24 breaths/min, respectively. A respiratory pattern classification algorithm combining feature-based random forest classifier and pattern discrimination algorithm was developed to classify different respiration patterns including eupnea, Cheyne-Stokes respiration, Kussmaul respiration and apnea. The overall classification accuracy of 90% was achieved on a test dataset. Finally, a real-time system showing RR and RP classification on a graphical user interface (GUI) was implemented for monitoring two subjects.

A 1.66Gb/s and 5.8pJ/b Transcutaneous IR-UWB Telemetry System with Hybrid Impulse Modulation for Intracortical Brain-Computer Interfaces.

Intra-cortical extracellular neural sensing is being rapidly and widely applied in several clinical research and brain-computer interfaces (BCIs), as the number of sensing channels continues to double every 6 years. By distributing multiple high-density extracellular micro-electrode arrays (MEAs) in vivo across the brain, each with 1000's of sensing channels, neuroscientists have begun to map the correlation of neuronal activity across different brain regions, with single-neuron precision [1]. Since each neural sensing channel typically samples at 20 to 50kS/s with a > 10b ADC, multiple MEAs demand a data transfer rate up to Gb/s [2]. However, these BCIs are severely hindered in many clinical uses due to the lack of a high-data-rate and miniature-wireless-telemetry solution that can be implanted below the scalp, i.e., transcutaneously (Fig. 24.2.1). The area of the wireless telemetry module should be miniaturized to ~3cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> due to neurosurgical implantation constraints. A transmission range up to 10cm is highly desirable, in order to improve the reliability of the wireless link against e.g., antenna misalignment, etc. Finally, the power consumption of the wireless telemetry should be limited to ~10mW to minimize thermal flux from the module's surface area, avoiding excessive tissue heating. Most of the conventional transcutaneous wireless telemetry systems adopt inductive coupling, but the data-rate is limited to a few Mb/s. A near-infrared (NIR) optical transcutaneous TX using a vertical-cavity-surface-emitting laser (VCSEL) [2] demonstrated a data-rate up to 300Mb/s but suffers from a limited transmission range (4mm) and requires a sub-mm precise alignment between the implant TX and a wearable RX. Impulse-radio UWB (IR-UWB) is promising for the targeted requirements [3]–[5].

Reliable and Low-Power Communications System Based on IR-UWB for Offshore Wind Turbines

In this paper, we propose the design of a low-power wireless sensor network architecture that enables robust communications inside offshore wind turbines. This research work is in the scope of the WATEREYE EU Project, where we have designed a corrosion monitoring solution to work unattended. The architecture is composed of several fixed sensor nodes, one mobile sensor node, several anchors and the WATEREYE Computer (WEC). Our approach is based on Impulse Radio Ultra wideband (IR-UWB) technology offering reliable and low-power communications in these harsh environments. On top of that, we propose a double star network using two UWB channels for the following purposes: one network for communications to send the sensor data and another one for ranging estimations to calculate the indoor positioning of the mobile sensor node. The power strategies applied to our system, at Hardware (HW) and Firmware (FW) levels, are described in detail. Furthermore, we present power consumption measurements obtaining the power profiles and the autonomy of the most important components of the proposed architecture supplied by battery. On the other hand, we describe the methodology to analyze the range, reliability and continuity of the two UWB links providing the packet loss and gaps as a function of distance. The proposed communications system has been validated in three different scenarios considering two of them very hostile environments. Furthermore, one of the scenarios is a real offshore wind turbine.

Non-Ionic Deep Learning-Driven IR-UWB Multiantenna Scheme for Breast Tumor Localization

This research proposes a deep learning-driven impulse radio ultra-wideband (IR-UWB) multiantenna scheme for non-ionic breast tumor localization. The structure of the multiantenna scheme consists of one side slot Vivaldi transmitting (Tx) and nine side slot Vivaldi receiving (Rx₁ &#x2013; Rx₉) antennas. To mitigate the attenuation and improve the diagnostic accuracy, the multiantenna scheme is rotated clockwise in 90&#x00B0; increments around the breast, with the angular position of the Tx antenna of 0&#x00B0;, 90&#x00B0;, 180&#x00B0;, and 270&#x00B0;. The deep learning algorithm is utilized to detect and localize the breast tumor, with 17 classification outputs, consisting of classifications 1 &#x2013; 16 which correspond to 16 vertically discretized segments of the breast and classification 17 for cancer-free. Experiments were carried out using heterogenous breast replicas with a tumor of 1 cm in diameter, and the breast replicas possess the dielectric property and Hounsfield units (HU) similar to those of human breasts. The experimental results were compared with the computed tomography (CT) scan images. The results reveal that the multiantenna scheme could efficiently detect and accurately localize the breast tumor for nearly all classifications, with the total accuracy (average of F1 scores) of 99.11 &#x0025;. Specifically, the novelty of this research lies in the use of deep learning with the IR-UWB technology to effectively localize breast tumors.

A Wireless Communication Network With a Ballistocardiography Experiment on the ISS: Scenario, Components and Preflight Demonstration

This paper introduces Wireless Compose-2 (WICO2), which is an experiment for the International Space Station (ISS) in order to demonstrate the provision of a flexible and adaptable wireless network infrastructure integrated into a commodity item to conduct and execute low-power, low-weight and wireless experiments in the scientific and medical domain. Recent work revealed a great potential in utilizing Wireless Sensor Network (WSN) in space habitats; however, the focus was only placed on sensing in the narrowband Industrial Scientific and Medical (ISM) 2.45 GHz band. This work extends these capabilities by utilizing Impulse Radio Ultra Wideband (IR-UWB) for ranging and evaluates the use of internal light sources for energy harvesting to drive the sensor nodes. The focus of the WICO2 experiment is on the operation of the scientific experiment Ballistocardiography for Extraterrestrial Applications and long-Term missions (BEAT), which is a demonstration of novel Ballistocardiography (BCG) sensors to monitor important cardiovascular parameters in a microgravity environment. Integrated in a Smart-Shirt, it will form a Body Area Network and make use of the IR-UWB communication to transmit the data to the network. This paper describes the concept and pre-flight test results to demonstrate the correct function, usability and performance of WICO2.

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.

An IR-UWB Multi-Sensor Approach for Collision Avoidance in Indoor Environments

This article aims to propose new techniques to detect and distinguish humans from moving machines in indoor environments. Although many research efforts have been already dedicated to humans’ indoor detection, most of the work has been focused on counting people and crowd measurement for consumer business applications. Our objective is to develop a reliable approach for humans’ indoor detection and localization aiming at avoiding collisions inside a mixed Industry 4.0 manned and unmanned environment to enhance personal and equipment safety and to prevent unwanted intrusions. An original aspect of our research is that we have worked on the real-time estimation of humans’ and moving machines’ positions while addressing the problems of multipath components and noise clutter detection. A multipulse constant false alarm rate detection algorithm is also proposed for removing the misdetections due to heavy clutter components in the indoor environment. Four impulse radio ultrawideband transceivers are placed in a specific geometry and data fusion is performed to reduce the influence of multipath and noise on the detection process. A convolutional neural network (CNN) is then used to extract the patterns corresponding to a moving machine and humans and classify them accordingly. Experiments have been carried out in two different indoor environments to demonstrate the performance of the proposed algorithms.

Sub-Milliwatt Transceiver IC for Transcutaneous Communication of an Intracortical Visual Prosthesis

An intracortical visual prosthesis plays a vital role in partially restoring the faculty of sight in visually impaired people. Reliable high date rate wireless links are needed for transcutaneous communication. Such wireless communication should receive stimulation data (downlink) and send out neural recorded data (uplink). Hence, there is a need for an implanted transceiver that is low-power and delivers sufficient data rate for both uplink and downlink. In this paper, we propose an integrated circuit (IC) solution based on impulse radio ultrawideband using on-off keying modulation (OOK IR-UWB) for the uplink transmitter, and binary phase-shift keying (BPSK) with sampling and digital detection for the downlink receiver. To make the solution low-power, predominantly digital components are used in the presented transceiver test-chip. Current-controlled oscillators and an impulse generator provide tunability and complete the on-chip integration. The transceiver test-IC is fabricated in 180 nm CMOS technology and occupies only 0.0272 mm2. At 1.3 V power supply, only 0.2 mW is consumed for the BPSK receiver and 0.3 mW for the IR-UWB transmitter in the transceiver IC, while delivering 1 Mbps and 50 Mbps, respectively. Our link budget analysis shows that this test chip is suitable for intracortical integration considering the future off-chip antennas/coils transcutaneous 3–7 mm communication with the outer side. Hence, our work will enable realistic wireless links for the intracortical visual prosthesis.

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.

A Low-Power Squaring Circuit with Regulated Output and Improved Settling Time in 180 nm CMOS for 3–5 GHz IR-UWB Applications

Abstract. This paper demonstrates a low-power squaring circuit for 3–5 GHz non-coherent Impulse-Radio Ultra-Wideband (IR-UWB) receivers for Pulse Position Modulation (PPM) in a low-cost 180 nm CMOS technology. The squaring, which is the key element in typical IR-UWB receivers, is performed exploiting the non-linear transfer function of a MOS transistor. For a high gain at low power consumption the transistor is biased in the moderate inversion region, where the second-order derivative of the transconductance gm and, as a result, the quadratic term in the transfer function reaches a maximum. A control loop was implemented to set the dc output voltage to a defined value and thus to allow a comparison of the squarer output signal with a defined threshold voltage, which can easily be set and adjusted (e.g. by a DAC). To speed up the settling time of the output and hence to reach higher data rates, a novel slew-rate booster is implemented at the output. Thereby, the squarer is capable of data rates of up to 15.6 Mbit s−1, which is more than two times higher compared to the circuit without the slew-rate booster, while only consuming 72.4 µW in addition. In the extracted post-layout simulations the whole circuitry consumes 724 µA at a 1.8 V power supply, resulting in a power consumption of 1.3 mW.

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.

Generation of low-complexity power-efficient IR-UWB waveforms

In this paper, a low-complexity approach is proposed for the generation of power efficient impulse radio ultra-wide band (IR-UWB) waveforms. The proposed approach relies on the optimization of the pulse width to achieve the highest power efficiency while maintaining FCC-compliant pulse shapes. Analytical expressions are developed for the power efficiency of widely adopted UWB waveforms, namely Gaussian and sech basis waveforms. The proposed approach is verified analytically and compared to results obtained through other approaches in the literature. It is shown that using as low as a single first order derivative Gaussian or sech basis waveform, with optimized pulse width can result in power efficiency as high as 85%, an efficiency that is achieved by other approaches using linear combinations of as much as 33 basis waveforms. This represents the low-complexity advantage of the proposed approach, which makes it more practical for implementation.

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.

걸어 다니는 사람의 생체신호탐지를 위한 움직임 보상기법 연구

The non-contact vital sign detection of an individual in indoor environments using a radar sensor has been widely researched by recent advances in the IoT industry. Most of these studies have been conducted by analyzing the spectrum of extracted phase variations in the echo signal of a motionless individual that has only micro-motion (that is, vital signs) caused by both the lung and the heart. However, in the case of a walking individual, the performance of the vital sign detection using conventional methods may be rapidly degraded owing to extreme phase fluctuations caused by the relatively large movement of the individual compared to the vital sign motions. To address this problem, we propose a new motion compensation method for the vital sign detection of a walking individual. First, we conduct a modeling of the echo signal for a walking individual using a radar and discuss problems in vital sign detection. Next, a new algorithm for real-time motion compensation is suggested based on a moving averaging filter and mode in statistics. In the experiments using an IR-UWB (Impulse-radio ultra-wideband) radar, we observed that the proposed method performed a successful real-time vital sign detection of a walking individual.

Dataset of human motion status using IR-UWB through-wall radar

Ultra-wideband (UWB) through-wall radar has a wide range of applications in non-contact human information detection and monitoring. With the integration of machine learning technology, its potential prospects include the physiological monitoring of patients in the hospital environment and the daily monitoring at home. Although many target detection methods of UWB through-wall radar based on machine learning have been proposed, there is a lack of an opensource dataset to evaluate the performance of the algorithm. This published dataset is measured by impulse radio UWB (IR-UWB) through-wall radar system. Three test subjects are measured in different environments and several defined motion status. Using the presented dataset, we propose a human-motion-status recognition method using a convolutional neural network (CNN), and the detailed dataset partition method and the recognition process flow are given. On the well-trained network, the recognition accuracy of testing data for three kinds of motion status is higher than 99.7%. The dataset presented in this paper considers a simple environment. Therefore, we call on all organizations in the UWB radar field to cooperate to build opensource datasets to further promote the development of UWB through-wall radar.