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

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

Remote Human Vital Sign Monitoring Using Multiple-Input Multiple-Output Radar at Millimeter-Wave Frequencies

Non-contact respiration rate (RR) and heart rate (HR) monitoring using millimeter-wave (mmWave) radars has gained lots of attention for medical, civilian, and military applications. These mmWave radars are small, light, and portable which can be deployed to various places. To increase the accuracy of RR and HR detection, distributed multi-input multi-output (MIMO) radar can be used to acquire non-redundant information of vital sign signals from different perspectives because each MIMO channel has different fields of view with respect to the subject under test (SUT). This dissertation investigates the use of a Frequency Modulated Continuous Wave (FMCW) radar operating at 77-81 GHz for this application. Vital sign signal is first reconstructed with Arctangent Demodulation (AD) method using phase change’s information collected by the radar due to chest wall displacement from respiration and heartbeat activities. Since the heartbeat signals can be corrupted and concealed by the third/fourth harmonics of the respiratory signals as well as random body motion (RBM) from the SUT, we have developed an automatic Heartbeat Template (HBT) extraction method based on Constellation Diagrams of the received signals. The extraction method will automatically spot and extract signals’ portions that carry good amount of heartbeat signals which are not corrupted by the RBM. The extracted HBT is then used as an adapted wavelet for Continuous Wavelet Transform (CWT) to reduce interferences from respiratory harmonics and RBM, as well as magnify the heartbeat signals. As the nature of RBM is unpredictable, the extracted HBT may not completely cancel the interferences from RBM. Therefore, to provide better HR detection’s accuracy, we have also developed a spectral-based HR selection method to gather frequency spectra of heartbeat signals from different MIMO channels. Based on this gathered spectral information, we can determine an accurate HR even if the heartbeat signals are significantly concealed by the RBM. To further improve the detection’s accuracy of RR and HR, two deep learning (DL) frameworks are also investigated. First, a Convolutional Neural Network (CNN) has been proposed to optimally select clean MIMO channels and eliminate MIMO channels with low SNR of heartbeat signals. After that, a Multi-layer Perceptron (MLP) neural network (NN) is utilized to reconstruct the heartbeat signals that will be used to assess and select the final HR with high confidence

Novel Methods for Weak Physiological Parameters Monitoring.

M.S. Thesis. University of Hawaiʻi at Mānoa 2017

High-Q Interstitial Square Coupled Microring Resonators Arrays

The properties of the square array of coupled Microring Resonators (MRRs) with interstitial rings are studied. Dispersion behavior of the interstitial square coupled MRRs is obtained through the transfer matrix method with the Floquet-Bloch periodic condition. Analytical formulas of the eigen wave vectors, band gaps and eigen mode vectors are derived for the special cases of the interstitial square coupled MRRs array with identical couplers and the regular square coupled MRRs array without the interstitial rings. Then, the eigen modes' field distribution are calculated for each of the four eigen wave vectors for a given frequency through the secular equation. Finally, numerical simulation is performed for an interstitial square coupled MRRs array with identical couplers and a regular square coupled MRRs array. The simulation result verifies the analytical analysis. Finally, the loaded quality factors of the interstitial 5-ring configuration, the regular 4-ring configuration and the 1-ring configuration are obtained. It is found that the loaded quality factor of the interstitial 5-ring configuration is up to 20 times and 8 times as high as those of the 1-ring configuration and the regular 4-ring configuration respectively, mainly due to the degenerated eigen modes at the resonant frequency. Thus, the interstitial square coupled MRRs array has the great potential to form high-quality integrated photonics components, including filters and resonance based sensing devices like the parity-time symmetric sensors.Comment: 17 pages, 8 figures, extended paper of a paper published at IEEE Journal of Quantum Electronics, vol. 56, no. 4, pp. 1-8, Aug. 2020, Art no. 6500208, doi: 10.1109/JQE.2020.298980

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