2D-Multiple Signal Processing Approach to Human Orientation Monitoring Using Millimeter-wave FMCW Radar

In recent years, unobtrusive continuous monitoring devices havebeen subjects of research interest. These sensors enable remotemonitoring of human vital signs and activity levels, which can beused in an expensive list of applications including the detection ofdistracted driving, gait analysis, and fall detection - applications thatare highly dependent on information regarding the posture of thesubject under test. In this work, a method of human posture orientation estimation is proposed using a high frequency mmwave(millimeter-wave) Frequency-Modulated Continuous Wave (FMCW)radar

Non-Contact Vital Sign Detection Using mm-Wave Radar

Vital Sign detection using radars has been a rising technology in the fields of healthcare, security, and military purposes. Typically, radars used for these tasks operate at lower frequencies due to their low cost and and the ability to detect behind obstacles, such as walls or undre debris. However, this leads to an overall large system as the lower the frequency of operation, the larger the size of the antennas. The system size increases when multiple antennas are used for subject localization. But, with the development of millimeter- wave radars and Antenna-on-Package (AoP) solutions, a more compact and portable radar is possible. In this thesis, a commercial, compact, and portable millimeter wave radar operating at 60 GHz is used to detect the vital signs of subjects. With the use of direction of arrival, beamforming, and frequency tracking, the millimeter wave radar is able to accurately detect the heart rate and respiration rate of subjects with high accuracy. Experiments are performed involving detection with varying distances, detection through drywall, and for a single or even multiple subjects

A CNN based Multifaceted Signal Processing Framework for Heart Rate Proctoring Using Millimeter Wave Radar Ballistocardiography

The recent pandemic has refocused the medical world's attention on the diagnostic techniques associated with cardiovascular disease. Heart rate provides a real-time snapshot of cardiovascular health. A more precise heart rate reading provides a better understanding of cardiac muscle activity. Although many existing diagnostic techniques are approaching the limits of perfection, there remains potential for further development. In this paper, we propose MIBINET, a convolutional neural network for real-time proctoring of heart rate via inter-beat-interval (IBI) from millimeter wave (mm-wave) radar ballistocardiography signals. This network can be used in hospitals, homes, and passenger vehicles due to its lightweight and contactless properties. It employs classical signal processing prior to fitting the data into the network. Although MIBINET is primarily designed to work on mm-wave signals, it is found equally effective on signals of various modalities such as PCG, ECG, and PPG. Extensive experimental results and a thorough comparison with the current state-of-the-art on mm-wave signals demonstrate the viability and versatility of the proposed methodology. Keywords: Cardiovascular disease, contactless measurement, heart rate, IBI, mm-wave radar, neural networkComment: 13 pages, 10 figures, Submitted to Elsevier's Array Journa

Noncontact monitoring of heartbeat and movements during sleep using a pair of millimeter-wave ultra-wideband radar systems

We experimentally evaluate the performance of a noncontact system that measures the heartbeat of a sleeping person. The proposed system comprises a pair of radar systems installed at two different positions. We use millimeter-wave ultra-wideband multiple-input multiple-output array radar systems and evaluate the performance attained in measuring the heart inter-beat interval and body movement. The importance of using two radar systems instead of one is demonstrated in this paper. We conduct three types of experiments; the first and second experiments are radar measurements of three participants lying on a bed with and without body movement, while the third experiment is the radar measurement of a participant actually sleeping overnight. The experiments demonstrate that the performance of the radar-based vital measurement strongly depends on the orientation of the person under test. They also show that the proposed system detects 70% of rolling-over movements made overnight

Vital Sign Monitoring in Dynamic Environment via mmWave Radar and Camera Fusion

Contact-free vital sign monitoring, which uses wireless signals for recognizing human vital signs (i.e, breath and heartbeat), is an attractive solution to health and security. However, the subject's body movement and the change in actual environments can result in inaccurate frequency estimation of heartbeat and respiratory. In this paper, we propose a robust mmWave radar and camera fusion system for monitoring vital signs, which can perform consistently well in dynamic scenarios, e.g., when some people move around the subject to be tracked, or a subject waves his/her arms and marches on the spot. Three major processing modules are developed in the system, to enable robust sensing. Firstly, we utilize a camera to assist a mmWave radar to accurately localize the subjects of interest. Secondly, we exploit the calculated subject position to form transmitting and receiving beamformers, which can improve the reflected power from the targets and weaken the impact of dynamic interference. Thirdly, we propose a weighted multi-channel Variational Mode Decomposition (WMC-VMD) algorithm to separate the weak vital sign signals from the dynamic ones due to subject's body movement. Experimental results show that, the 90${^{th}}$ percentile errors in respiration rate (RR) and heartbeat rate (HR) are less than 0.5 RPM (respirations per minute) and 6 BPM (beats per minute), respectively

Recent Advances in mmWave-Radar-Based Sensing, Its Applications, and Machine Learning Techniques: A Review

Human gesture detection, obstacle detection, collision avoidance, parking aids, automotive driving, medical, meteorological, industrial, agriculture, defense, space, and other relevant fields have all benefited from recent advancements in mmWave radar sensor technology. A mmWave radar has several advantages that set it apart from other types of sensors. A mmWave radar can operate in bright, dazzling, or no-light conditions. A mmWave radar has better antenna miniaturization than other traditional radars, and it has better range resolution. However, as more data sets have been made available, there has been a significant increase in the potential for incorporating radar data into different machine learning methods for various applications. This review focuses on key performance metrics in mmWave-radar-based sensing, detailed applications, and machine learning techniques used with mmWave radar for a variety of tasks. This article starts out with a discussion of the various working bands of mmWave radars, then moves on to various types of mmWave radars and their key specifications, mmWave radar data interpretation, vast applications in various domains, and, in the end, a discussion of machine learning algorithms applied with radar data for various applications. Our review serves as a practical reference for beginners developing mmWave-radar-based applications by utilizing machine learning techniques.publishedVersio

Simultaneous Monitoring of Multiple People's Vital Sign Leveraging a Single Phased-MIMO Radar

Vital sign monitoring plays a critical role in tracking the physiological state of people and enabling various health-related applications (e.g., recommending a change of lifestyle, examining the risk of diseases). Traditional approaches rely on hospitalization or body-attached instruments, which are costly and intrusive. Therefore, researchers have been exploring contact-less vital sign monitoring with radio frequency signals in recent years. Early studies with continuous wave radars/WiFi devices work on detecting vital signs of a single individual, but it still remains challenging to simultaneously monitor vital signs of multiple subjects, especially those who locate in proximity. In this paper, we design and implement a time-division multiplexing (TDM) phased-MIMO radar sensing scheme for high-precision vital sign monitoring of multiple people. Our phased-MIMO radar can steer the mmWave beam towards different directions with a micro-second delay, which enables capturing the vital signs of multiple individuals at the same radial distance to the radar. Furthermore, we develop a TDM-MIMO technique to fully utilize all transmitting antenna (TX)-receiving antenna (RX) pairs, thereby significantly boosting the signal-to-noise ratio. Based on the designed TDM phased-MIMO radar, we develop a system to automatically localize multiple human subjects and estimate their vital signs. Extensive evaluations show that under two-subject scenarios, our system can achieve an error of less than 1 beat per minute (BPM) and 3 BPM for breathing rate (BR) and heartbeat rate (HR) estimations, respectively, at a subject-to-radar distance of $1.6~m$. The minimal subject-to-subject angle separation is $40{\deg}$, corresponding to a close distance of $0.5~m$ between two subjects, which outperforms the state-of-the-art

Non-Contact Detection of Vital Signs Based on Improved Adaptive EEMD Algorithm (July 2022)

Non-contact vital sign detection technology has brought a more comfortable experience to the detection process of human respiratory and heartbeat signals. Ensemble empirical mode decomposition (EEMD) is a noise-assisted adaptive data analysis method which can be used to decompose the echo data of frequency modulated continuous wave (FMCW) radar and extract the heartbeat and respiratory signals. The key of EEMD is to add Gaussian white noise into the signal to overcome the mode aliasing problem caused by original empirical mode decomposition (EMD). Based on the characteristics of clutter and noise distribution in public places, this paper proposed a static clutter filtering method for eliminating ambient clutter and an improved EEMD method based on stable alpha noise distribution. The symmetrical alpha stable distribution is used to replace Gaussian distribution, and the improved EEMD is used for the separation of respiratory and heartbeat signals. The experimental results show that the static clutter filtering technology can effectively filter the surrounding static clutter and highlight the periodic moving targets. Within the detection range of 0.5 m similar to 2.5 m, the improved EEMD method can better distinguish the heartbeat, respiration, and their harmonics, and accurately estimate the heart rate

Monitoring of the heart movements using a FMCW radar and correlation with an ECG

Monitoring the heart activity is an important task to prevent and diagnose cardiovascular diseases. An electrocardio-gram (ECG) is the gold standard for such task. It monitors the heart electrical activity, and while the later is highly correlated to the cardiac mechanical activity, it does not provide all the information. Other sensors such as echo-cardiograph allow to monitor the heart movements, but such tools are hard to operate and expensive. Therefore, contact-less monitoring of the heart using RF sensing has gained interest over the past years. In this paper, we provide a process to extract the movement of the heart with a high accuracy from a millimeter wave radar, i.e. we describe a non invasive and affordable way to monitor cardiac movements. We then demonstrate the correlation between the observed movements and the ECG. Furthermore, we propose an algorithm to synchronize the ECG signal and the processed signal from the radar sensor. The results we obtained provide insights on the mechanical activity of the heart, which could assist cardiologists in their diagnosisComment: 10 pages, 19 figure

In-Cabin Radar Monitoring System: Detection and Localization of People Inside Vehicle using Vital Sign Sensing Algorithm

Radars are used in automobiles for various functionalities, starting from the obstacle alarm during vehicle reversing to advanced functionalities like autonomous driving. A practical method for monitoring people inside a vehicle for various applications (surveillance, safety, etc.) could be built using Radar. This paper presents the embedded implementation of a vital sign sensing algorithm using the radar signal processing (RSP) technique. MEX (MATLAB executable) interface is performed with the embedded C code of the vital sign sensing algorithm generated for validating the results with the RSP technique. Finally, Unit testing is performed on the developed embedded C code of the vital sign sensing algorithm to remove the dead codes and to verify whether all branches and statements in a developed algorithm are working accordingly. The embedded C code results were found to be matching precisely with the RSP technique. With the help of obtained results, we can differentiate between an adult and a baby inside a vehicle