Vital Detection Research Articles

Indoor Localization for Passive Moving Objects Based on a Redundant SIMO Radar Sensor

In the past decade, many efforts have been devoted to indoor localization solutions. While significant progresses have been achieved, short-range localization of passive moving objects in indoor environments remains a technical challenge, especially when available radio spectrum is limited. In this paper, we resolve this challenge by proposing a single-frequency continuous-wave Doppler radar sensor implemented with a redundant single-input multiple-output architecture. Since only Doppler phase shift information is used in the proposed tracking-localization solution, the localization is naturally immune to stationary clutters such as walls and furniture, making this indoor localization only responsive to moving objects. Only using a single frequency is required, such an indoor localization can be implemented with the highest spectrum and energy efficiency. The obtained results imply the potential for the detection of human activities and vital signs in indoor environments with strong stationary scatters.

Demonstration of a Speckle Based Sensing with Pulse-Doppler Radar for Vibration Detection.

In previous works, an optical technique for extraction and separation of remote static vibrations has been demonstrated. In this paper, we will describe an approach in which RF speckle movement is used to extract remote vibrations of a static target. The use of conventional radar Doppler methods is not suitable for detecting vibrations of static targets. In addition, the speckle method has an important advantage, in that it is able to detect vibrations at far greater distances than what is normally detected in classical optical methods. The experiment described in this paper was done using a motorized vehicle, which engine was turned on and off. The results showed that the system was able to distinguish between the different engine states, and in addition, was able to determine the vibration frequency of the engine. The first step towards real time detection of human vital signs using RF speckle patterns is presented.

Doppler Frequency Estimation by Parameterized Time-Frequency Transform and Phase Compensation Technique

Doppler frequency can reflect the dynamic properties of the radar target and thus provides an effective approach to analyze the target signatures. In this paper, a novel method is proposed to accurately estimate Doppler frequencies of multi-component radar signals. The method employs a more general model, i.e., the redundant Fourier model to represent various Doppler frequency trajectories. In the first stage of the method, a multi-component signal is decomposed into some individual components using a de-chirping method. Then, the parameterized time-frequency transform (PTFT) with a matched Fourier kernel is designed to estimate frequencies of the obtained components. With the estimated frequencies, the complex envelopes of the components can then be estimated through a joint-least-squares method. We show that the phase information of the obtained envelopes can be used for error compensation in the frequency estimation. The proposed method integrating the PTFT with the phase compensation technique can significantly improve the accuracy of Doppler frequency estimation. Both simulated and experimental examples are provided, indicating the potential of the method in analyzing Doppler signals with overlapped components. The future study will focus on the real applications of the method for radar target detection and monitoring, such as human vital sign detection and condition monitoring of industrial equipments (e.g., wind turbines).

A Wearable Self-Injection-Locked Sensor With Active Integrated Antenna and Differentiator-Based Envelope Detector for Vital-Sign Detection From Chest Wall and Wrist

This paper presents a new wearable self-injection-locked sensor with a self-oscillating active integrated antenna (AIA) and a differentiator-based envelope detector for detecting vital signs from the chest wall and the wrist. The AIA is designed to radiate the electromagnetic signal and sense the scattered-back signal, which is phase modulated by the physiological movement of the human chest and wrist. The received self-injection signal from the subject under test locks the AIA and simultaneously introduces a variation in the output magnitude (amplitude modulation) and a shift in the oscillation frequency (frequency modulation). In other words, the output signal of the AIA is a frequency-modulated carrier with an amplitude-varying envelope. To acquire the vital signs from the modulated signal, an envelope detector is integrated with a microwave differentiator to form a differentiator-based envelope detector, which demodulates signal. The heartbeat and wrist pulse rates measured by the proposed sensor agree well with the results acquired by a finger pulse oximeter. Since the proposed demodulator has a simple architecture, it has a potential to be integrated with the AIA in a multilayered printed-circuit board for the realization of a compact wearable self-injection-locked radar sensor.

Detection of human vital signs in hazardous environments by means of video magnification.

In cases of natural disasters, epidemics or even in dangerous situations like an act of terrorism, battle fields, a shooting or a mountain accident, finding survivors is a challenge. In these kind of situations it is sometimes critical to know if a person has vital signs or not, without the need to be in contact with the victim, thus avoiding jeopardizing the lives of the rescue workers. In this work, we propose the use of video magnification techniques to detect small movements in human bodies due to breathing that are invisible to the naked eye. Two different video magnification techniques, intensity-based and phase-based, were tested. The utility of these techniques to detect people who are alive but injured in risk situations was verified by simulating a scene with three people involved in an accident. Several factors such as camera stability, distance to the object, light conditions, magnification factor or computing time were analyzed. The results obtained were quite positive for both techniques, intensity-based method proving more adequate if the interest is in almost instant results whereas the phase-based method is more appropriate if processing time is not so relevant but the degree of magnification without excessive image noise.

Literature review on wireless sensing-Wi-Fi signal-based recognition of human activities

With the rapid development and wide deployment of wireless technology, Wi-Fi signals have no longer been confined to the Internet as a communication medium. Wi-Fi signals will be modulated again by human actions when propagating indoors, carrying rich human body state information. Therefore, a novel wireless sensing technology is gradually emerging that can realize gesture recognition, human daily activity detection, identification, indoor localization and human body tracking, vital signs detection, imaging, and emotional recognition by extracting effective feature information about human actions from Wi-Fi signals. Researchers mainly use channel state information or frequency modulated carrier wave in their current implementation schemes of wireless sensing technology, called “Walls have eyes”, and these schemes cover radio-frequency technology, signal processing technology, and machine learning. These available wireless sensing systems can be used in many applications such as smart home, medical health care, search-and-rescue, security, and with the high precision and passively device-free through-wall detection function. This paper elaborates the research actuality and summarizes each system structure and the basic principles of various wireless sensing applications in detail. Meanwhile, two popular implementation schemes are analyzed. In addition, the future diversely application prospects of wireless sensing systems are presented.

Electromagnetic Modeling of Vital Sign Detection and Human Motion Sensing Validated by Noncontact Radar Measurements

Technology to analyze human motion has significantly advanced over the last few years. Locomotion studies have proved useful and have been practically applicable to some gait analysis. However, its use has been hampered by the amount of data generated and the limitations to the interpretation capabilities of this massive data. New techniques need to be developed to capture data rapidly, accurately, efficiently, and preferably remotely using noninvasive techniques. It is our goal to develop modeling capabilities and experimentally validate results to succeed in establishing models and practical ways to interpret this collected data. In this paper, electromagnetic models have been developed and validated using a software-defined radar to analyze different human motion sensing scenarios. Encouraging preliminary results have been achieved, and efforts will continue to analyze and model more motion scenarios.

60-GHz CMOS Doppler Radar Sensor With Integrated V-Band Power Detector for Clutter Monitoring and Automatic Clutter-Cancellation in Noncontact Vital-Signs Sensing

This paper presents the design of a V-band logarithmic power detector (PD) and the integration with a 60-GHz CMOS vital-signs Doppler radar sensor to be incorporated with a microprocessor control unit (MCU) for fast automatic clutter cancellation. The PD adopts the successive detection logarithmic amplifier (SDLA) topology and achieves a high dynamic range by replacing the limited amplifiers with millimeter-wave (MMW) linear amplifiers. From the measurement results, the PD exhibits a dynamic range and logarithmic errors higher than 35 dB and within ±2 dB from 50 to 62 GHz. The whole integrated radar sensor chip is fabricated by a 90-nm CMOS process with a chip size of 2 mm $\times2.34$ mm and a dc power consuming of 243 mW. The radar chip and a 60-GHz 17-dBi patch-array antenna are integrated by bondwire interconnection on a compact single carrier board for experimental test. By incorporating the MCU to the radar chip board, the fast automatic clutter canceling can achieve more than 25-dB clutter cancellation. Moreover, it shows a successful vital-signs detection for a distance more than 1.2 m.

Probabilities of False Alarm for Vital Sign Detection on the Basis of a Doppler Radar System.

Vital detection on the basis of Doppler radars has drawn a great deal of attention from researchers because of its high potential for applications in biomedicine, surveillance, and finding people alive under debris during natural hazards. In this research, the signal-to-noise ratio (SNR) of the remote vital-sign detection system is investigated. On the basis of different types of noise, such as phase noise, Gaussian noise, leakage noise between the transmitting and receiving antennae, and so on, the SNR of the system has first been examined. Then the research has focused on the investigation of the detection and false alarm probabilities of the system when the transmission link between the human and the radar sensor system took the Nakagami-m channel model. The analytical model for the false alarm and the detection probabilities of the system have been derived. The proposed theoretical models for the SNR and detection probability match with the simulation and measurement results. These theoretical models have the potential to be used as good references for the hardware development of the vital-sign detection radar sensor system.

`Through-wall human being detection using UWB impulse radar

Ultra-wideband (UWB) impulse radar plays an important role in contactless vital sign (VS) detection. The VS can be extracted remotely by acquiring the oscillations in the human chest. Unfortunately, it is usually challenging to identify VS due to the low signal-to-noise ratio (SNR) only based on the traditional fast Fourier transform (FFT) especially in complicated conditions. To extract VS accurately, this paper presents a new scheme by analyzing the skewness characteristic of the received UWB impulses, which are modulated by life activities. The distance from the human subject to the radar antenna can be calculated by performing the discrete short-time Fourier transform (DSFT) on skewness. The frequency of human respiratory movement can be estimated based on the developed ensemble empirical mode decomposition (EEMD)-based accumulation technique by canceling out the harmonics effectively. The performance of the developed detection method is tested with several experiments carried out in different environments.

A Fundamental-and-Harmonic Dual-Frequency Doppler Radar System for Vital Signs Detection Enabling Radar Movement Self-Cancellation

This paper proposes and presents a cost-effective fundamental-and-harmonic dual-frequency (FHDF) Doppler radar system for vital signs detection from a mobile radar platform. The proposed FHDF radar architecture concurrently transmits the fundamental signal component (FSC) and its inherent second harmonic signal component (SHSC) of the voltage-controlled oscillator toward opposite directions. The FSC is transmitted toward a target in motion, while the SHSC is transmitted toward a stationary low-cost reflector. For the receiver, a coherent dual-band dual-low-IF architecture is derived and proposed for the first time. This architecture can concurrently receive the reflected FSC that is phase modulated by both motions of the target and the radar platform, and the reflected SHSC that is phase modulated only by the motion of the radar platform, without aliasing, thus enabling the reduction of the overall radar size, power consumption, and system cost. Through the subtraction of a pertinent information contained in the received SHSC from the information embedded in the received FSC using an adaptive noise cancellation technique, the resulting signal contains the desired information that is free of motion artifacts from a moving radar platform. Experimental results show that the proposed radar system is capable of extracting human vital signs, including respiration and heartbeat, even in the presence of a large radar platform movement. By using the proposed FHDF radar system, a see-through-wall vital signs detection from a mobile radar platform can also be successfully conducted without using any additional sensors.

Algorithms for Monitoring Heart Rate and Respiratory Rate From the Video of a User's Face.

Smartphone cameras can measure heart rate (HR) by detecting pulsatile photoplethysmographic (iPPG) signals from post-processing the video of a subject’s face. The iPPG signal is often derived from variations in the intensity of the green channel as shown by Poh et. al. and Verkruysse et. al.. In this pilot study, we have introduced a novel iPPG method where by measuring variations in color of reflected light, i.e., Hue, and can therefore measure both HR and respiratory rate (RR) from the video of a subject’s face. This paper was performed on 25 healthy individuals (Ages 20–30, 15 males and 10 females, and skin color was Fitzpatrick scale 1–6). For each subject we took two 20 second video of the subject’s face with minimal movement, one with flash ON and one with flash OFF. While recording the videos we simultaneously measuring HR using a Biosync B-50DL Finger Heart Rate Monitor, and RR using self-reporting. This paper shows that our proposed approach of measuring iPPG using Hue (range 0–0.1) gives more accurate readings than the Green channel. HR/Hue (range 0–0.1) (n}{}r=0.9201n, n}{}pn-value = 4.1617, and RMSE = 0.8887) is more accurate compared with HR/Green (n}{}r=0.4916n, n}{}pn-value = 11.60172, and RMSE = 0.9068). RR/Hue (range 0–0.1) (n}{}r=0.6575n, n}{}pn-value = 0.2885, and RMSE = 3.8884) is more accurate compared with RR/Green (n}{}r=0.3352n, n}{}pn-value = 0.5608, and RMSE = 5.6885). We hope that this hardware agnostic approach for detection of vital signals will have a huge potential impact in telemedicine, and can be used to tackle challenges, such as continuous non-contact monitoring of neo-natal and elderly patients. An implementation of the algorithm can be found at https://pulser.thinkbiosolution.com

Nonintrusive Vital Signs Monitoring for Sleep Apnea Patients: A Preliminary Study

Sleep is a fundamental and vital physiological function. Getting enough quality sleep is necessary to a person’s mental health, physiological well-being, quality of life, and safety. Sleep-disordered breathing, specifically obstructive sleep apnea can result in serious health issues, including hypertension and stroke. The current approaches for diagnosing sleep disorders are burdensome, intrusive, and can affect the patient’s sleep quality. As a result, there is a crucial need for less cumbersome systems to diagnose sleep-related problems. In this paper, we evaluated the capacity of the microbend fiber optic sensor to monitor heart rate and respiration in a nonintrusive manner. In addition, we tested the capacity of the sensor in discriminating between shallow breathing and no breathing. The proposed sensor was compared with the three-channel portable monitoring device (ApneaLink) in a clinical setting during a drug-induced sleep endoscopy. Across all ten patients recruited for our study, the system achieved satisfactory results in the mean heart rate and the mean respiratory rate with an error of 0.55 ± 0.59 beats/minute and 0.38 ± 0.32 breaths/minute, respectively. Besides, the Pearson correlation coefficient between the proposed sensor and the reference device was 0.96 and 0.78 for heart rate and respiration, respectively. On the contrary, the proposed sensor provided a very low sensitivity (24.24 ± 12.81%) and a relatively high specificity (85.88 ± 6.01%) for sleep apnea detection. It is expected that this preliminary research will pave the way toward unobtrusive detection of vital signs in real time.

Microw(h)att?! Ultralow-Power Six-Port Radar: Realizing Highly Integrated Portable Radar Systems with Good Motion Sensitivity at Relatively Low Cost

Short-range noncontact microwave radar systems have undergone significant development in recent years [1]. Driven by advances in modern monolithic microwave integrated circuitry, printed circuit board (PCB) technologies, and embedded computing, highly integrated portable radar systems with good motion sensitivity can today be realized at relatively low cost. Radar systems have emerged in a variety of new application fields including industrial sensing [2], [3], human vital-sign detection [4], [5], and structural health monitoring [6].

Doppler Radar Vital Signs Detection Method Based on Higher Order Cyclostationary.

Due to the non-contact nature, using Doppler radar sensors to detect vital signs such as heart and respiration rates of a human subject is getting more and more attention. However, the related detection-method research meets lots of challenges due to electromagnetic interferences, clutter and random motion interferences. In this paper, a novel third-order cyclic cummulant (TOCC) detection method, which is insensitive to Gaussian interference and non-cyclic signals, is proposed to investigate the heart and respiration rate based on continuous wave Doppler radars. The k-th order cyclostationary properties of the radar signal with hidden periodicities and random motions are analyzed. The third-order cyclostationary detection theory of the heart and respiration rate is studied. Experimental results show that the third-order cyclostationary approach has better estimation accuracy for detecting the vital signs from the received radar signal under low SNR, strong clutter noise and random motion interferences.

Improved denoising method for through-wall vital sign detection using UWB impulse radar

Noncontact vital sign detection is widely used in finding victims in post-disaster search and rescue, through-wall surveillance (TWS), and medical diagnosis and monitoring. Human subject can be remotely sensed by extracting life activities such as respiration and heartbeat. However, the signal to noise ratio (SNR) is often low, particularly in complex environments, which results in errors in both range and respiration frequency (RF) estimation. To improve the accuracy, an improved system for the vital sign detection is presented which is based on impulse ultra-wideband (UWB) radar. The range is determined based on the short-time Fourier transform (STFT) of the standard deviation of the received signals. Further, the ensemble empirical mode decomposition (EEMD) based frequency accumulation (FA) technique is presented to determine RF. Performance results are presented which show that the proposed method is superior to several well-known techniques.

Single Self-Injection-Locked Radar With Two Antennas for Monitoring Vital Signs With Large Body Movement Cancellation

This paper presents a novel self-injection-locked radar with two antennas to transmit and retransmit a continuous wave to opposite sides of a human body for monitoring vital signs in real time with large body movements. The system can prevent the nonlinear effects of large Doppler phase shifts on the cancellation of body motion artifacts, to extract accurate vital signs from the received Doppler-shifted signal. However, the effectiveness of the cancellation is limited by the received clutter echoes in this system. Therefore, the system also has a clutter effect elimination circuit to improve substantially the detection of vital signs in an examinee at various positions between the two antennas even when the examinee moves often by more than one wavelength.

Accurate Measurement in Doppler Radar Vital Sign Detection Based on Parameterized Demodulation

Utilizing Doppler radar to conduct noncontact vital sign detection has attracted growing interest in recent years. Aiming to extract the vital sign information from the baseband signal effectively and accurately, a novel signal processing method based on parameterized demodulation (PD) is proposed. To effectively characterize the baseband signal whose phase consists of two oscillating components (i.e., the respiration and heartbeat components), the proposed algorithm defines a demodulation operator with sine kernel functions and formulates the phase demodulation as a parameter optimization problem. To increase the computational efficiency of the algorithm, the parameters corresponding to the respiration and heartbeat components are estimated sequentially. Specifically, the respiration component is first estimated and removed from the phase of the baseband signal, and then, the heartbeat component is extracted from the residual signal. Compared with the existing methods, the proposed algorithm is free of the resolution problem in fast Fourier transform-based methods with a limited data length and can obtain accurate rate tracking in a noisy environment. Both simulated and experimental results are provided to demonstrate the advantages and effectiveness of the proposed method for accurate noncontact vital sign detection.

Interview

Prof. Zhike Peng from Shanghai Jiao Tong University, talks to Electronics Letters about the background to his paper ‘Static clutter elimination for FMCW radar displacement measurement based on phasor offset compensation’, page 1491. Prof. Zhike Peng Currently, part of my research focuses on signal processing, microwave detection, and micro-Doppler analysis, in particular for microwave/RF radar vibration monitoring, biomedical, and some other industrial applications. To realise non-contact vibration and movement monitoring, we found microwave radar to be a good approach, as it is low-cost and has high robustness against harsh environments. Fortunately, benefiting from the technical advances in high-frequency printed circuit board and hardware miniaturisation technology, microwave radar modules are widely used in various applications. Therefore, we try to use microwave radar, as an effective short-range sensor to perform non-contact displacement tracking. Specifically, we use frequency-modulated continuous-wave (FMCW) radar to measure the displacement variation in vibration motion. Currently, using phase-based radar interferometry technique, FMCW radar has excellent capacity to measure small displacement variation. However, since the radar antenna has a finite radiation beam-width, i.e. it cannot be a point, all targets within the radar radiation coverage reflect echoes and the radar baseband signal is the sum of multiple components. Generally, FMCW radar can isolate different targets with a different range bin, but struggles to isolate components generated by targets in the same bin. Reflections from stationary objects (i.e. static clutter) near the desired target interfere with the wanted signals backscattering from the moving scatterers at the same range, causing the estimated displacement time series to be smaller than the truth. When the stationary object and the desired target have comparable reflective performance, the static clutter interference has a significant impact on the estimation accuracy. In our Letter, a post-processing procedure based on phasor offset compensation (POC) is proposed to eliminate the static clutter interference. It can be implemented easily by using an updated signal processing algorithm and achieves accurate displacement measurement. Previously, for this problem existing works mainly focus on the hardware side, such as installing a strong reflection point, or using frequency-shift transponders, which increase hardware complexity and is limited in real-life applications. Comparatively, our POC technique is a novel way to solve the static clutter interference problem from a signal processing side. This work can be directly applied in FMCW radar displacement measurement, e.g. vibration monitoring, large structural health monitoring, slow movement detection and vital sign detection. In the long run, the POC technique can be extended in other phase-based measurement system for a wide variety of applications. Currently, one portion of our research group is working on radar signal processing algorithms to achieve good performance in related applications. Specifically, we are now working on the research of non-contact vibration monitoring and structural health monitoring based on FMCW radar, and micro-Doppler analysis for pattern recognition. In the near future, we would like to extend our research in radar imaging and environmental perception. Then, we would like to investigate the information fusion technique combined with the camera, to obtain better and more intelligent environmental imaging and recognition. In the next 10 years, we would like to see more integrated software defined radar developing platforms and advanced radar signal processing algorithms for a variety of applications. Moreover, we expect to see intelligent radar detection systems with multiple type radars. Various sensors have their own advantages and drawbacks, therefore we need to see intelligent information fusion techniques to achieve better environmental imaging and recognition. This includes object recognition and physical quantity measurement, which is greatly beneficial to autopilot, robot vision and other fields. We would like to develop advanced signal processing methods, intelligent information fusion techniques and several prototypes of intelligent sensor to boost the development in this field.

Metrological Characterization for Vital Sign Detection by a Bioradar

In space missions, during the long isolation at extreme conditions for human health, it is of paramount importance to monitor vital parameters. One such parameter is the breathing rate. Indeed, several factors can induce some breathing anomalies during the sleep, which may cause apnea episodes. In order to act timely with the right therapy, an early diagnosis is required. Conventional devices are usually uncomfortable since they require electrodes or probes in contact with the subject. An alternative way to perform this kind of measurement in a remote sensing modality is provided by a continuous wave bioradar operating in the microwave frequency band. This is an effective contactless tool for monitoring the respiratory activity through the measurement of chest deformation due to inhalation and exhalation. The radar emits a low power electromagnetic wave at a single frequency, which is reflected by the human chest. By measuring of the phase shift between the incident and reflected wave, it is possible to detect and monitor the respiratory rate. The main contribution of this work is concerned with a metrological characterization of the continuous wave bioradar; which is a topic not thoroughly assessed in the relevant literature. In particular, the bioradar measurements are also compared with data recorded by a spirometer, which is a standard medical device that measures the air volume inhaled and exhaled by the subject. The purpose of this study is the characterization of the measurement standard uncertainty to enable the assessment of the bioradar system performance.