Detection Of Human Vital Signs Research Articles

Contactless and short‐range vital signs detection with doppler radar millimetre‐wave (76–81 GHz) sensing firmware

AbstractVital signs such as heart rate (HR) and respiration rate (RR) are essential physiological parameters that are routinely used to monitor human health and bodily functions. They can be continuously monitored through contact or contactless measurements performed in the home or a hospital. In this study, a contactless Doppler radar W‐band sensing system was used for short‐range, contactless vital sign estimation. Frequency‐modulated continuous wave (FMCW) measurements were performed to reduce the influence of a patient's micromotion. Sensing software was developed that can process the received chirps to filter and extract heartbeat and breathing rhythm signals. The proposed contactless sensing system eliminates the need for the contact electrodes, electric patches, photoelectric sensors, and conductive wires used in typical physiological sensing methods. The system operates at 76–81 GHz in FMCW mode and can detect objects on the basis of changes in frequency and phase. The obtained signals are used to precisely monitor a patient's HR and RR with minimal noise interference. In a laboratory setting, the heartbeats and breathing rhythm signals of healthy young participants were measured, and their HR and RR were estimated through frequency‐ and time‐domain analyses. The experimental results confirmed the feasibility of the proposed W‐band mm‐wave radar for contactless and short‐range continuous detection of human vital signs.

Contactless face video based vital signs detection framework for continuous health monitoring using feature optimization and hybrid neural network.

Continuous monitoring of vital signs such as respiration and heart rate is essential to detect and predict conditions that may affect the patient's well-being. To detect these vital signs most medical systems use contact sensors. They are not feasible for long term monitoring and are not repeatable. Vital signs using facial video-noncontact monitoring are becoming increasingly important. Researchers in the last few years although considerable progress has been made, challenging datasets absence timing of assessment process and the technology still has some limitations such as time consuming nature and lack of computer portability. To solve those problems, we propose a contactless video based vital signs detection framework for continuous health monitoring using feature optimization and hybrid neural network. In the proposed technique, modified war strategy optimization algorithm is proposed to segment the face portion from the input video frames. Then, we utilize the known data acquisition models to extract vital signs from the segmented face portions are heart rate, blood pressure, respiratory rate and oxygen saturation. An improved neural network structure (Lifting Net) is further used to achieve the adaptive extraction of deep hidden features for specific signs, for realizing the high precision of human health monitoring. The Hughes effect or dimensionality issue affects detection accuracy in sign classification when there are fewer training instances relative to the number of spectral features. The problem can be overcome through feature optimization here Northern goshawk optimization algorithm is used to select optimal best features which reduces the data dimensionality issue. Furthermore, hybrid deep ensemble reinforcement learning classifier is proposed for the human vital sign detection and classification which ensures the early detection of patient abnormality. Finally, we validate our framework using benchmark video datasets such as TokyoTechrPPG, PURE and COHFACE. To proves the effectiveness of proposed technique using simulation results and comparative analysis.

Non-intrusive Human Vital Sign Detection Using mmWave Sensing Technologies: A Review

Non-invasive human vital sign detection has gained significant attention in recent years, with its potential for contactless, long-term monitoring. Advances in radar systems have enabled non-contact detection of human vital signs, emerging as a crucial area of research. The movements of key human organs influence radar signal propagation, offering researchers the opportunity to detect vital signs by analyzing received electromagnetic (EM) signals. In this review, we provide a comprehensive overview of the current state-of-the-art in millimeter-wave (mmWave) sensing for vital sign detection. We explore human anatomy and various measurement methods, including contact and non-contact approaches, and summarize the principles of mmWave radar sensing. To demonstrate how EM signals can be harnessed for vital sign detection, we discuss four mmWave-based vital sign sensing (MVSS) signal models and elaborate on the signal processing chain for MVSS. Additionally, we present an extensive review of deep learning-based MVSS and compare existing studies. Finally, we offer insights into specific applications of MVSS (e.g., biometric authentication) and highlight future research trends in this domain.

Study on the environmental effect on human being detection using the UWB bio-radar

Objective: Non-contact penetrating detection and sensing of human beings (vital signs) through nonmetallic obstacles (ruins, wall, and smog) using the ultra-wideband (UWB) bio-radar plays a significant role in various post-disaster rescue operations in national public security events, like earthquake, building collapse, and factory explosion. In practical application scenarios, the narrowband radio frequency interference (RFI) and surrounding movement interference (SMI) are the two most common and major types of environmental interference (EI), which would cause serious effects on the penetrating detection performance of the UWB bio-radar.Methods: Therefore, through establishing a quantitative and controllable experiment system and combining a proposed quantitative evaluation method, this paper quantitatively investigates and evaluates the influencing characteristics and laws of these two interferences on human vital sign detection using the UWB bio-radar. In the quantitative experiments, two key environmental spatial parameters (interference distance and angle) of interference sources and two kinds of system parameters (main lobe width and time window) of the UWB bio-radar are considered.Results: Numerous experiments at different interference positions and corresponding statistical results demonstrated that both the RFI and SMI have a high negative correlation with the interference distance. Meanwhile, the SMI, which is highly related to the interference angle, could be screened or isolated by the detecting time window and main lobe boundary of UWB bio-radar, but the RFI could not be detected.Discussion: This study shows potential in providing transcendental knowledge and guidance for special EI suppression study, and even for the future design, manufacture, and rational use of the UWB bio-radar, facilitating improvement in the practical performance and effectiveness of the UWB bio-radar search and rescue system.

Noncontact Sleeping Heartrate Monitoring Method Using Continuous-Wave Doppler Radar Based on the Difference Quadratic Sum Demodulation and Search Algorithm.

Continuous-wave doppler radar, which has the advantages of simple structure, low cost, and low power consumption, has attracted extensive attention in the detection of human vital signs. However, while respiration and heartbeat signals are mixed in the echo phase, the amplitude difference between the two signals is so large that it becomes difficult to measure the heartrate (HR) from the interference of respiration stably and accurately. In this paper, the difference quadratic sum demodulation method is proposed. According to the mixed characteristics of respiration and heartbeat after demodulation, the heartbeat features can be extracted with the help of the easy-to-detect breathing signal; combined with the constrained nearest neighbor search algorithm, it can realize sleeping HR monitoring overnight without body movements restraint. Considering the differences in vital-sign characteristics of different individuals and the irregularity of sleep movements, 54 h of sleep data for nine nights were collected from three subjects, and then compared with ECG-based HR reference equipment. After excluding the periods of body turning over, the HR error was within 10% for more than 70% of the time. Experiments confirmed that this method, as a tool for long-term HR monitoring, can play an important role in sleeping monitoring, smart elderly care, and smart homes.

VitRad: A low-cost continuous wave Doppler radar system with 3D-printed horn antennas for human vital sign detection

In this study, a low-cost continuous wave (CW) radar system with 3D-printed high-gain horn antennas called VitRad is proposed for human vital sign detection. The CW radar consists of 3D-printed high-gain horn antennas, commercially available low-cost surface-mounting devices, and monolithic ICs. The CW radar system operates at a frequency band of 5.8 GHz, and the backscattered I/Q data are collected using a digital storage oscilloscope (DSO). The data is processed on MATLAB to determine vital sign information such as respiratory and heartbeat rates. It is demonstrated that the proposed CW radar system for vital-sign monitoring can effectively measure respiratory and heartbeat rates.

Design of a Planar Antenna Array with Wide Bandwidth and Narrow Beamwidth for IR-UWB Radar Applications

This paper presents the design of an H-band planar antenna array with broad bandwidth and narrow beam width for an IR-UWB radar application. The basic single wideband microstrip antenna is achieved by adding slots and the inset-fed technique. Then, we proposed a planar antenna array on a limited area that obtains an essential narrow beamwidth for the radar of a Non-Contact Human Vital Signs Detection application. The experimental and simulated results of the microstrip antenna array are in good agreement. The measured results show that the proposed antenna array exhibits a wide impedance bandwidth of 10.7% at around 7.5 GHz and a narrow beamwidth of 40 degrees vertically and 50 degrees horizontally, respectively.

Kalman Filter and Cross-Multiply Algorithm With Adaptive DC Offset Removal

Recently, microwave radar technologies are taking more important roles in human vital signs monitoring. The modified differentiate and cross multiply is the state-of-the-art demodulation algorithm to extract the phase shifts of the microwave signal modulated by vital signs. However, this method suffers from the interference of the direct current (dc) offset. In order to solve this problem, a Kalman filter and cross-multiply (KFCM) approach is proposed. With a Kalman filter, the dc bias of the I/Q channel could be estimated and removed. Moreover, a real-time circle fitting algorithm is raised to compensate for the loss of phase information. Simulation results show that the proposed KFCM algorithm effectively eliminates the coupling caused by the dc offset and improves the performance of demodulation in the presence of noise. Finally, two practical scenarios, including real-time human vital sign detection and long-term breathing monitoring during sleep, are demonstrated to show the potential applications of this algorithm.

Vital Sign Detection in Any Orientation Using a Distributed Radar Network via Modified Independent Component Analysis

Recent advances in radar systems have demonstrated the possibility of noncontact human vital signs detection. Most prior research has focused on vital sign detection with specific subjects’ orientations. So far, very few reported studies have been carried out on vital sign detection in any orientation, which is necessary to be applicable in various scenarios, e.g., assisting in the daily life of elderly persons living alone. To tackle this problem, this article presents a scheme that uses a distributed radar network, which transmits stepped frequency continuous-wave (SFCW) signals, then collecting the vital sign data returned from different directions of the subject and subsequently obtaining the respiratory and heartbeat rates from these collected data. A new respiratory and heartbeat model is proposed here for the theoretical analysis and simulation of vital sign detection for any orientation utilizing a distributed radar network. A modified independent component analysis (ICA) method noted as derivative independent component analysis (DICA) is proposed here to estimate the respiratory and heartbeat rates from multiple channels of the distributed radar network. Use of the ICA method was theoretically validated first to prove that the problem formulation satisfies the three conditions required for its implementation. Vital signs of a stationary subject were detected in simulation and measurement using multiple receiving antennas that are fan-shaped distributed around the subject from different directions. In this scenario, both the heartbeat and respiratory rates can be accurately estimated using the proposed DICA algorithm. Moreover, we also experimentally investigated the performance of the DICA method with different combinations of channels as input signals to the DICA algorithm. Both the simulated and experimental results have demonstrated that, by using a distributed radar network and the DICA method, a successful and accurate respiratory and heartbeat rate detection of a subject regardless of subject’s orientation can be achieved due to the independence of their non-Gaussian sources.

Noncontact Vital Sign Detection With UAV-Borne Radars: An Overview of Recent Advances

Airborne radar carried on board unmanned aerial vehicles (UAVs) is serving as the harbinger of new remote sensing applications for security and rescue in inclement environments. The mobility and agility of UAVs, along with intelligent onboard sensors (cameras, acoustics, and radar), are more effective during the early stages of disaster response. The ability of radars to penetrate through objects and operate in low-visibility conditions enables the detection of occluded human subjects on and under debris when other sensing modalities fail. Recently, radars have been deployed on UAVs to measure minute human physiological parameters, such as respiratory and heart rates while sensing through clothing and building materials. Signal processing techniques are critical in enabling UAV-borne radars for human vital sign detection (VSD) in multiple operation modes. UAV radar interferometry provides valuable VSD in both hovering and flying motions. In the synthetic aperture radar (SAR) configuration, UAV-based VSD is available at a high spatial resolution. Novel radar configurations, such as in through-material sensing, UAV swarm, and tethered UAVs, are required to penetrate obstacles, facilitate multitasking, and allow for high endurance, respectively. This article provides an overview of the recent advances in UAV-borne VSD, with a focus on the deployment modes and processing methods.

Microstrip patch and Sierpinski fractal antennas: simulations and experimental characterization for vital signs detection with Doppler radar technique

Objectives: To fabricate planar models of antennas with resonance frequencies in the ultrahigh frequency band that allow short-range detection of respiration and heartbeat by a simple continuous wave Doppler radar system. Methods: Models of antennas were created in CST Studio software and the main parameters were computed. Then, antennas were fabricated at a PCB prototyping machine and were experimentally characterized in an anechoic chamber. Total efficiency and radiation patterns indicated the best working frequencies of 2.12 GHz and 8.82 GHz respectively, to test the human vital signs detection with a continuous-wave Doppler radar technique in direct visibility conditions. Findings: The patch antenna at 2.12 GHz had a maximum gain of 3.15 dBi and total efficiency of 43% while the Sierpinski antenna at 8.82 GHz had a maximum gain of 5.5 dBi and total efficiency of 65%. At incident power densities on the human subject’s chest of 4.5 x 10-4 mW/m2 and of 2.6 x 10-2 mW/m2 respectively, the Doppler radar system based on these antennas offered precise responses. Practically, it was possible to extract both the heartbeat rate and the respiration rate, by simply applying the classical FFT algorithm on a time-series phase data of the transmission coefficient recorded during 30 seconds, when the set-up was composed of just the antennas and a vector network analyzer. Novelty: Increased detection accuracy was obtained due to careful characterization of the antennas parameters with no need of special processing algorithms. Keywords: Sierpinski Antenna; Patch Antenna; CW Doppler Radar; Respiration Rate; Heartbeat Rate

Vital Sign Detection and Radar Self-Motion Cancellation Through Clutter Identification

This article presents a novel technique to remove the radar self-motion effects (RSMs) for an accurate detection of human vital signs. As opposed to the commonly used techniques, the proposed approach does not require any additional sensor, and instead, it extracts the RSM from the signals reflected by stationary clutters. Since the proposed technique requires to accurately identify the clutter range, two procedures for its automatic identification are proposed, aimed to detect both small and large radar motions. Besides allowing precise and reliable vital sign detection, it provides a compact, lightweight, comfortable, and cost-effective solution since potentially intrusive additional sensors are not required. Simulations have been carried out for validating the proposed approach, with an insight on the influence of different clutter radar cross sections on the sensitivity. Moreover, the effectiveness of the RSM cancellation has been experimentally demonstrated, showing its suitability for different applications, e.g., radar on moving platforms, vibrating tools, handheld devices, unmanned aerial vehicles, and cars.

CMOS Class C amplifier for motion detection

Noncontact detection of human vital signs based on miniaturized Doppler radar systems (DRSs) can be widely used in healthcare and biomedical applications. Although significant progresses have been achieved, a reliable wireless vital signs detection in the presence of large-scale random human body movements remains a technical challenge. This work proposes a cost-effective UWB Doppler radar system for movement detection from a mobile radar platform. The proposed radar architecture concurrently transmits the fundamental signal component (FSC) and it use a Complex Wavelet Transform algorithm for change detection using an Ultra-Wideband (UWB) impulse. CWT based approach is better than the existing method and it takes less response time which is more suitable for detection with high accuracy. Signal Peak Analysis & Time Interval Estimation performed to find out normalized magnitude change detection of signal. UWB radar works well in poor visibility conditions. Spectral constraints placed on radar systems by regulatory agencies require the design of highly linear amplifiers. Class C amplifier contributes important spectral part in radar system. In this work also design cascaded multiple threshold CMOS class C amplifier for motion detection. Experimental results show that the proposed radar system is capable of extracting human movement, even in the presence of a large radar platform movement with high SNR and accuracy rates.

A Survey on Vital Signs Detection Using Radar Techniques and Processing With FPGA Implementation

This paper presents a survey of the state-of-the-art advances in human vital signs detection using radar sensors, their integration and coexistence with communication systems, and their issues in spectrum sharing. The focus of this survey is to review the detection, monitoring, and tracking of vital signs, specifically the respiration rate and heartbeat rate over the recent five years. It is observed that in line with technological advancements, a multitude of radar types operating in diverse frequency spectra have been introduced with different hardware implementations, considering various detection scenarios, and applying multiple signal processing algorithms. The aim of these researches varies, from enhancing the detection accuracy, improving the processing speed, reducing the power consumption, simplifying the hardware used, lowering implementation costs, and the combinations of them. Besides that, this review also focuses on literature aimed at increasing the detection accuracy and reducing the processing time using FPGAs, prior to benchmarking them against other processing platforms. Finally, a perspective on the future of human vital signs detection using radar sensors concludes this review.

How Far Are We from Achieving Self‐Powered Flexible Health Monitoring Systems: An Energy Perspective

AbstractThe rapid development of flexible electronics and sensing technologies have made it possible for the detection of various human vital signs and biomarkers through an extremely light‐weight and soft device attached to the skin. To support such sensing devices for multi‐parameter tracking and continuous operation, a powerful energy supply unit, which is compact, mobile, and with high energy density and self‐charging capability, is desired. The recently emerged energy harvesting devices have demonstrated the potential of utilizing ambient and human‐based energy sources to power sensor systems. In this context, this review aims to investigate the question of whether the capacity of current flexible energy devices can meet the energy demand of wireless flexible sensor systems in long‐term health monitoring applications. First, the total energy demand of typical sensor systems is estimated by analyzing the energy consumption of each building block of the relevant systems. The design of the energy supply architecture is discussed by considering batteries/supercapacitors as the energy storage unit and photovoltaic, thermoelectric, piezoelectric, and triboelectric devices as the energy‐harvesting unit. Based on the analysis, health monitoring protocols that could be readily realized with self‐powered system designs are suggested and the core challenges for further development of the technologies for practical applications are identified.

Phase-Extraction Method With Multiple Frequencies of FMCW Radar for Human Body Motion Tracking

A phase-extraction technique with multiple frequencies of a frequency-modulated continuous-wave (FMCW) radar is proposed, which allows the detection of target motions exactly. An FMCW chirp signal is decomposed into multiple-frequency continuous-wave (CW) signals to extract modulated phases due to target motions. The demodulated phases at multiple frequencies are summed and averaged. Since the proposed method can extract the phase without performing a fast Fourier transform (FFT), it can overcome the range migration problem, which causes an error in phase extraction using a conventional FFT-based method. Therefore, the proposed method can reduce spurious noises induced by phase errors compared with the conventional method. This was verified experimentally for both human vital sign detection and body motion tracking.

Vital Sign Detection during Large-Scale and Fast Body Movements Based on an Adaptive Noise Cancellation Algorithm Using a Single Doppler Radar Sensor.

The non-contact detection of human vital signs (i.e., respiration rate (RR) and heartbeat rate (HR)) using a continuous-wave (CW) Doppler radar sensor has great potential for intensive care monitoring, home healthcare, etc. However, large-scale and fast random body movement (RBM) has been a bottleneck for vital sign detection using a single CW Doppler radar. To break this dilemma, this study proposed a scheme combining adaptive noise cancellation (ANC) with polynomial fitting, which could retrieve the weak components of both respiration and heartbeat signals that were submerged under serious RBM interference. In addition, the new-type discrete cosine transform (N-DCT) was introduced to improve the detection accuracy. This scheme was first verified using a numerical simulation. Then, experiments utilizing a 10-GHz Doppler radar sensor that was built from general-purpose radio frequency (RF) and communication instruments were also carried out. No extra RF/microwave components and modules were needed, and neither was a printed circuit board nor an integrated-chip design required. The experimental results showed that both the RR and HR could still be extracted during large-scale and fast body movements using only a single Doppler radar sensor because the RBM noises could be greatly eliminated by utilizing the proposed ANC algorithm.

Human Vital Signs Detection Methods and Potential Using Radars: A Review.

Continuous monitoring of vital signs, such as respiration and heartbeat, plays a crucial role in early detection and even prediction of conditions that may affect the wellbeing of the patient. Sensing vital signs can be categorized into: contact-based techniques and contactless based techniques. Conventional clinical methods of detecting these vital signs require the use of contact sensors, which may not be practical for long duration monitoring and less convenient for repeatable measurements. On the other hand, wireless vital signs detection using radars has the distinct advantage of not requiring the attachment of electrodes to the subject’s body and hence not constraining the movement of the person and eliminating the possibility of skin irritation. In addition, it removes the need for wires and limitation of access to patients, especially for children and the elderly. This paper presents a thorough review on the traditional methods of monitoring cardio-pulmonary rates as well as the potential of replacing these systems with radar-based techniques. The paper also highlights the challenges that radar-based vital signs monitoring methods need to overcome to gain acceptance in the healthcare field. A proof-of-concept of a radar-based vital sign detection system is presented together with promising measurement results.

Contact-Free Biosignal Acquisition via Capacitive and Ultrasonic Sensors

Contact-free detection of human vital signs like heart rate and respiration rate will improve the patients' comfort and enables long-term monitoring of newborns or bedridden patients. For that, reliable and safe measurement techniques are indispensable. The aim of this work is the development and comparison of novel ultrasonic and capacitive measurement setups, sharing a common hardware platform. Both measurement techniques that are implemented and compared are based on the detection of minor chest wall vibrations in millimeter ranges, due to geometrical thorax changes during respiration and heartbeat activities. After examining the physical measurement conditions and simulating the capacitive sensor, a problem-specific measurement setup is proposed. The system is characterized to be capable of detecting distance changes below 2 {\mu}m via the ultrasonic sensor and below 800 {\mu}m via the capacitive sensor. First subject measurements show that the detection of heart activities is possible under ideal conditions and exclusively with the proposed ultrasonic approach. However, the capacitive sensor works reliably for respiration monitoring, even when the subject is fully-clothed and covered with a blanket. The chosen ultrasonic approach is sensitive regarding minor changes of the reflecting surface and therefore has high uncertainty. In contrast, capacitive respiration detection is very reliable. It is conceivable that improvements in the capacitive sensor circuitry will also enable the detection of heart activities. The proposed ultrasonic approach presents current problems of this technique. In contrast to that, the unusual approach of capacitive sensing demonstrates a high potential regarding vital signs acquisition.

Flexible wearable graphene/alginate composite non-woven fabric temperature sensor with high sensitivity and anti-interference

The development of a sensitive flexible wearable temperature sensor has extraordinary significance in the field of human vital signs detection. Herein, we demonstrate a flexible temperature sensor (FTS) fitting on human skin based on non-woven fabric, and it has been systematically characterized and tested for performance. The resistor-type FTS integrated sodium alginate with graphene, behaved as negative temperature coefficient feature and exhibited excellent sensing performance of good sensitivity (TCR = − 1.5 °C−1), high accuracy of 0.1 °C, short response time (26.3 s), high stability and repeatability at the temperature range of 20.0–45.0 °C. Specially, the FTS eliminated strain and humidity interference, so that can be attached to the human body to detect skin temperature changes continuously and stably.