[Microwave Heartprint: A novel non-contact human identification technology based on cardiac micro-motion detection using ultra wideband bio-radar].

The existing one-time identity authentication technology cannot continuously guarantee the legitimacy of user identity during the whole human-computer interaction session, and often requires active cooperation of users, which seriously limits the availability. This study proposes a new non-contact identity recognition technology based on cardiac micro-motion detection using ultra wideband (UWB) bio-radar. After the multi-point micro-motion echoes in the range dimension of the human heart surface area were continuously detected by ultra wideband bio-radar, the two-dimensional principal component analysis (2D-PCA) was exploited to extract the compressed features of the two-dimensional image matrix, namely the distance channel-heart beat sampling point (DC-HBP) matrix, in each accurate segmented heart beat cycle for identity recognition. In the practical measurement experiment, based on the proposed multi-range-bin & 2D-PCA feature scheme along with two conventional reference feature schemes, three typical classifiers were selected as representatives to conduct the heart beat identification under two states of normal breathing and breath holding. The results showed that the multi-range-bin & 2D-PCA feature scheme proposed in this paper showed the best recognition effect. Compared with the optimal range-bin & overall heart beat feature scheme, our proposed scheme held an overall average recognition accuracy of 6.16% higher (normal respiration: 6.84%; breath holding: 5.48%). Compared with the multi-distance unit & whole heart beat feature scheme, the overall average accuracy increase was 27.42% (normal respiration: 28.63%; breath holding: 26.21%) for our proposed scheme. This study is expected to provide a new method of undisturbed, all-weather, non-contact and continuous identification for authentication.

Remote Emotion Recognition Using Continuous-Wave Bio-Radar System.

The Bio-Radar is herein presented as a non-contact radar system able to capture vital signs remotely without requiring any physical contact with the subject. In this work, the ability to use the proposed system for emotion recognition is verified by comparing its performance on identifying fear, happiness and a neutral condition, with certified measuring equipment. For this purpose, machine learning algorithms were applied to the respiratory and cardiac signals captured simultaneously by the radar and the referenced contact-based system. Following a multiclass identification strategy, one could conclude that both systems present a comparable performance, where the radar might even outperform under specific conditions. Emotion recognition is possible using a radar system, with an accuracy equal to 99.7% and an F1-score of 99.9%. Thus, we demonstrated that it is perfectly possible to use the Bio-Radar system for this purpose, which is able to be operated remotely, avoiding the subject awareness of being monitored and thus providing more authentic reactions.

A Novel Non-Contact Detection and Identification Method for the Post-Disaster Compression State of Injured Individuals Using UWB Bio-Radar.

Building collapse leads to mechanical injury, which is the main cause of injury and death, with crush syndrome as its most common complication. During the post-disaster search and rescue phase, if rescue personnel hastily remove heavy objects covering the bodies of injured individuals and fail to provide targeted medical care, ischemia-reperfusion injury may be triggered, leading to rhabdomyolysis. This may result in disseminated intravascular coagulation or acute respiratory distress syndrome, further leading to multiple organ failure, which ultimately leads to shock and death. Using bio-radar to detect vital signs and identify compression states can effectively reduce casualties during the search for missing persons behind obstacles. A time-domain ultra-wideband (UWB) bio-radar was applied for the non-contact detection of human vital sign signals behind obstacles. An echo denoising algorithm based on PSO-VMD and permutation entropy was proposed to suppress environmental noise, along with a wounded compression state recognition network based on radar-life signals. Based on training and testing using over 3000 data sets from 10 subjects in different compression states, the proposed multiscale convolutional network achieved a 92.63% identification accuracy. This outperformed SVM and 1D-CNN models by 5.30% and 6.12%, respectively, improving the casualty rescue success and post-disaster precision.

Frequency Comb-Based Ground-Penetrating Bioradar: System Implementation and Signal Processing.

Radars can be used as sensors to detect the breathing of victims trapped under layers of building materials in catastrophes like earthquakes or gas explosions. In this contribution, we present the implementation of a novel frequency comb continuous wave (FCCW) bioradar module using a commercial software-defined radio (SDR). The FCCW radar transmits multiple equally spaced frequency components simultaneously. The data acquisition of the received combs is frequency domain-based. Hence, it does not require synchronization between the transmit and receive channels, as time domain-based broadband radars, such as ultra wideband (UWB) pulse radar and frequency-modulated CW (FMCW) radar, do. Since a frequency comb has an instantaneous wide bandwidth, the effective scan rate is much higher than that of a step frequency CW (SFCW) radar. This FCCW radar is particularly suitable for small motion detection. Using inverse fast Fourier transform (IFFT), we can decompose the received frequency comb into different ranges and remove ghost signals and interference of further range intervals. The frequency comb we use in this report has a bandwidth of only 60 MHz, resulting in a range resolution of up to 2.5 m, much larger than respiration-induced chest wall motions. However, we demonstrate that in the centimeter range, motions can be detected and evaluated by processing the received comb signals. We want to integrate the bioradar into an unmanned aircraft system for fast and safe search and rescue operations. As a trade-off between ground penetrability and the size and weight of the antenna and the radar module, we use 1.3 GHz as the center frequency. Field measurements show that the proposed FCCW bioradar can detect an alive person through different nonmetallic building materials.

Evaluation of a non-contact ultra-wideband bio-radar sleep monitoring device for screening of sleep breathing disease.

PURPOSE: Ultra-wideband bio-radar (UWB) is a new non-contact technology that can be used to screen for obstructive sleep apnea (OSA). However, little information is available regarding its reliability. This study aimed to evaluate the effectiveness of UWB and to determine if UWB could provide a novel and reliable method for the primary screening of sleep-related breathing disorders. METHOD: Subjects with suspected OSA from the sleep center of the First Hospital of the China Medical University were assessed over the period of September 2018 to April 2019 for enrollment in the study. Three detection methods were simultaneously used, including the STOP-Bang questionnaire (SBQ), UWB, and standard polysomnography (PSG). The data were analyzed using a fourfold table, receiver operating characteristic curves, Spearman rank correlation coefficients, Bland-Altman plots, and epoch-by-epoch analysis. RESULT: Of 67 patients, 56 were men, mean age was 43 ± 11 years, mean body mass index was 27.8 ± 4.8 kg/m2, and mean SBQ score was 4.8 ± 1.6. The apnea-hypopnea index (AHI) (r = 0.82, p < 0.01) and minimum arterial oxygen saturation (r = 0.80, p < 0.01) of the UWB were positively correlated with those obtained from the PSG. UWB performed better than SBQ, as indicated by the larger area under the curve (0.85 vs. 0.632). The sensitivity and specificity of the UWB-AHI were good (100%, 70%, respectively). CONCLUSIONS: UWB performs well in the screening of OSA and can provide reliable outcomes for the screening of OSA at the primary level.

24 GHz Flexible Antenna for Doppler Radar-Based Human Vital Signs Monitoring.

Noncontact monitoring of human vital signs has been an emerging research topic in recent years. A key approach to this monitoring is the use of the Doppler radar concept which enables real-time vital signs detection, resulting in a new class of radar system known as bio-radar. The antennas are a key component of any bio-radar module and their designs should meet the common requirements of bio-radar applications such as high radiation directivity and mechanical flexibility. This paper presents the design of a four-element antenna array on a flexible liquid crystal polymer (LCP) substrate of 100 µm thickness and εr of 3.35. The designed antenna array can be used with a 24 GHz bio-radar for vital signs monitoring in a non-contact manner. It features a relatively compact size of 36.5 × 53 mm2 and measured gain of 5.81 dBi. The two vital signs: breathing rate (BR) and heart rate (HR) of two human subjects are detected with relatively good accuracy using the fabricated antenna array and radio frequency (RF) output power of -3 dBm from a distance of approximately 60 cm. The effect of bending on the antenna performance is also analyzed.

Textile Antenna for Bio-Radar Embedded in a Car Seat.

A bio-radar system is presented for vital signs acquisition, using textile antennas manufactured with a continuous substrate that integrates the ground plane. Textile antennas were selected to be used in the RF (Radio Frequency) front-end, rather than those made of conventional materials, to further integrate the system in a car seat cover and thus streamline the industrial manufacturing process. The development of the novel substrate material is described in detail, as well as its characterization process. Then, the antenna design considerations are presented. The experiments to validate the textile antennas operation by acquiring the respiratory signal of six subjects with different body structures while seated in a car seat are presented. In conclusion, it was possible to prove that bio-radar systems can operate with textile-based antennas, providing accurate results of the extraction of vital signs.

Fall Detection Using Multiple Bioradars and Convolutional Neural Networks.

A lack of effective non-contact methods for automatic fall detection, which may result in the development of health and life-threatening conditions, is a great problem of modern medicine, and in particular, geriatrics. The purpose of the present work was to investigate the advantages of utilizing a multi-bioradar system in the accuracy of remote fall detection. The proposed concept combined usage of wavelet transform and deep learning to detect fall episodes. The continuous wavelet transform was used to get a time-frequency representation of the bio-radar signal and use it as input data for a pre-trained convolutional neural network AlexNet adapted to solve the problem of detecting falls. Processing of the experimental results showed that the designed multi-bioradar system can be used as a simple and view-independent approach implementing a non-contact fall detection method with an accuracy and F1-score of 99%.

Research on Non-Contact Monitoring System for Human Physiological Signal and Body Movement.

With the rapid increase in the development of miniaturized sensors and embedded devices for vital signs monitoring, personal physiological signal monitoring devices are becoming popular. However, physiological monitoring devices which are worn on the body normally affect the daily activities of people. This problem can be avoided by using a non-contact measuring device like the Doppler radar system, which is more convenient, is private compared to video monitoring, infrared monitoring and other non-contact methods. Additionally real-time physiological monitoring with the Doppler radar system can also obtain signal changes caused by motion changes. As a result, the Doppler radar system not only obtains the information of respiratory and cardiac signals, but also obtains information about body movement. The relevant RF technology could eliminate some interference from body motion with a small amplitude. However, the motion recognition method can also be used to classify related body motion signals. In this paper, a vital sign and body movement monitoring system worked at 2.4 GHz was proposed. It can measure various physiological signs of the human body in a non-contact manner. The accuracy of the non-contact physiological signal monitoring system was analyzed. First, the working distance of the system was tested. Then, the algorithm of mining collective motion signal was classified, and the accuracy was 88%, which could be further improved in the system. In addition, the mean absolute error values of heart rate and respiratory rate were 0.8 beats/min and 3.5 beats/min, respectively, and the reliability of the system was verified by comparing the respiratory waveforms with the contact equipment at different distances.

Challenges and Potential Solutions of Psychophysiological State Monitoring with Bioradar Technology.

Psychophysiological state monitoring provides a promising way to detect stress and accurately assess wellbeing. The purpose of the present work was to investigate the advantages of utilizing a new unobtrusive multi-transceiver system on the accuracy of remote psychophysiological state monitoring by means of a bioradar technique. The technique was tested in laboratory conditions with the participation of 35 practically healthy volunteers, who were asked to perform arithmetic and physical workload tests imitating different types of stressors. Information about any variation in vital signs, registered by a bioradar with two transceivers, was used to detect mental or physical stress. Processing of the experimental results showed that the designed two-channel bioradar can be used as a simple and relatively easy approach to implement a non-contact method for stress monitoring. However, individual specificity of physiological responses to mental and physical workloads makes the creation of a universal stress-detector classifier that is suitable for people with different levels of stress tolerance a challenging task. For non-athletes, the proposed method allows classification of calm state/mental workload and calm state/physical workload with an accuracy of 89% and 83% , respectively, without the usage of any additional a priori information on the subject.

Research status of contact-free detection technology of human walking gait based on bio-radar / 国际生物医学工程杂志

Human gait involves a complex mechanism of muscular skeletal coordinated operation,which is specific and can be used as the basis of identity recognitions and clinical disease diagnoses.Human gaits have wide application value in the field of disaster rescue,battlefield ambulance,counter-terrorism,security,and medical and healthcare.The traditional contact-free gait detection technology mainly depends on optical images or ultrasound,which is susceptible to light,low visibility,obstacles,etc.In recent years,with the rapidly development of bio-radar technology,the bio-radar based contact-free human gait signal detection technology has shown more advantages.It can not be affected by light,can penetrate clothing,camouflage or even walls,and can operate in all-weathe,including low visibility weather conditions such as smog,smoke and fog.In this paper,the technical principles and methods of bio-radar based contact-free human gait detection technologies were discussed,the research status was summarized,and the development trendency was prospected.

Research on targets distinguishing and locating technique via UWB bioradar based on pixel segmentation algorithm / 医疗卫生装备

Objective To explore the feasibility and applicable conditions of one-dimensional distance distinction combining pixel segmentation algorithm in multi-target recognition and identification of multi-channel ultra wide-band bioradar.Methods The signals the radar received were decomposed,reconstructed and filtered,and one-dimensional distance distinction algorithm was applied to achieving multi-target distance discrimination,then multi-target two-dimensional positioning was achieved based on the principle of angle determination applying pixel segmentation algorithm.Finally,a target positioning experiment was executed by collecting 10 volunteers using the method above.Results The experiment indicated that pixel segmentation algorithm gained advantages when used for positioning a single target while disadvantages for multi target.Concluslon It's proved that it's feasible to locate three or less targets with one-dimensional distance distinction as well as two-dimensional locating based on pixel segmentation algorithm.Positioning accuracies are different in case of numbers of targets.

Research on targets distinguishing and locating technique via UWB bioradar based on pixel segmentation algorithm / 医疗卫生装备

Objective To explore the feasibility and applicable conditions of one-dimensional distance distinction combining pixel segmentation algorithm in multi-target recognition and identification of multi-channel ultra wide-band bioradar.Methods The signals the radar received were decomposed,reconstructed and filtered,and one-dimensional distance distinction algorithm was applied to achieving multi-target distance discrimination,then multi-target two-dimensional positioning was achieved based on the principle of angle determination applying pixel segmentation algorithm.Finally,a target positioning experiment was executed by collecting 10 volunteers using the method above.Results The experiment indicated that pixel segmentation algorithm gained advantages when used for positioning a single target while disadvantages for multi target.Concluslon It's proved that it's feasible to locate three or less targets with one-dimensional distance distinction as well as two-dimensional locating based on pixel segmentation algorithm.Positioning accuracies are different in case of numbers of targets.

Experimental study on shadowing effect of multi static human targets respiration detection with UWB bio-radar / 医疗卫生装备

Objective To study the shadowing effect when using UWB bio-radar to detect multiple static human targets to solve the problem in multi-target detection.Methods With simulated breathing apparatus as detection targets,the UWB bioradar multi static targets respiration detection experiment was designed,and the influences of distance and angle between targets and its respiratory frequency and amplitude on the shadowing effect were studied.Result The shadowing effect was mainly affected by the relative position of the multiple targets,while the respiratory frequency and amplitude of the target had less influence on it.Conclusion When multi static human targets are detected the shadowing effect does exist,and the effect mainly derives from the block of electromagnetic wave by the front target,while the change of respiratory parameters of the front target has little influence on the effect.

Cognitive bio-radar: The natural evolution of bio-signals measurement.

In this article we discuss a novel approach to Bio-Radar, contactless measurement of bio-signals, called Cognitive Bio-Radar. This new approach implements the Bio-Radar in a Software Defined Radio (SDR) platform in order to obtain awareness of the environment where it operates. Due to this, the Cognitive Bio-Radar can adapt to its surroundings in order to have an intelligent usage of the radio frequency spectrum to improve its performance. In order to study the feasibility of such implementation, a SDR based Bio-Radar testbench was developed and evaluated. The prototype is shown to be able to acquire the heartbeat activity and the respiratory effort. The acquired data is compared with the acquisitions from a Biopac research data acquisition system, showing coherent results for both heartbeat and breathing rate.

Novel method to simulate micro-motion of human body surface via precision linear module / 医疗卫生装备

To design a new method to simulate the micro-motion of human body surface due to respiration and heartbeat, and to provide detection object and calibration signal for the bio-radar technology. Precision lin-ear module was used to transform rotational movement to linear displacement, with AC servo motor to precisely control the module's rotation. Ultimately, ultralow-frequency micro-motion was produced with its displacement being quantitatively controlled. A system simulating the micro-motion of human body surface was newly built. Compared with the old system, the new one produced micro-motion with better constancy, and realized quantitative control of the motion's dis-placement. The method lays technological foundation for simulating the micro-motion of human body surface due to respiration and heartbeat and may promote the development of bio-radar technology towards intensive and compre-hensive levels.

Experimental study on effects of different breathing modes on separation of bioradar signals / 医疗卫生装备

To separate the biological radar echo signals in different breathing modes. Echo signals of bioradar-based vital signs monitoring system were acquired in different breathing modes, and an improved signal separa-tion algorithm was used to obtain respiratory and heartbeat signals as well as their parameters. Under two breath-ing modes, the center frequencies of the signals from the self-adaptive filter were kept consistent with those of heartbeat signals, and the signals with other frequencies were suppressed effectively. The algorithm can be used to sep-arate heartbeat signals while suppress other interference signals effectively. ZHANG Jing and LIU Qian are the first au-thors who contributed equally to the article.

Research progress of signal processing method for bioradar / 国际生物医学工程杂志

Bioradar is based on the integration of theory of radar and bio-medical engineering,which can detect the life-parameters in farther distance.It is new concept radar presented by foreign experts.The technology can be widely used in detection of lifeform signal and non-contact clinical monitoring.Biomedical signal processing method is the premise that the technology can be realized.The signals can be interfered by the environmental factors,breath motion and so on,especially the influences of breath motion which can not be solved by average methods.A large number of signal processing methods are used in various aspects of the technology.This review introduces the progress of bioradar technology and the application of the current signal processing methods in the field.