Doppler radar-based non-contact health monitoring for obstructive sleep apnea diagnosis: A comprehensive review

Today’s rapid growth of elderly populations and aging problems coupled with the prevalence of obstructive sleep apnea (OSA) and other health related issues have affected many aspects of society. This has led to high demands for a more robust healthcare monitoring, diagnosing and treatments facilities. In particular to Sleep Medicine, sleep has a key role to play in both physical and mental health. The quality and duration of sleep have a direct and significant impact on people’s learning, memory, metabolism, weight, safety, mood, cardio-vascular health, diseases, and immune system function. The gold-standard for OSA diagnosis is the overnight sleep monitoring system using polysomnography (PSG). However, despite the quality and reliability of the PSG system, it is not well suited for long-term continuous usage due to limited mobility as well as causing possible irritation, distress, and discomfort to patients during the monitoring process. These limitations have led to stronger demands for non-contact sleep monitoring systems. The aim of this paper is to provide a comprehensive review of the current state of non-contact Doppler radar sleep monitoring technology and provide an outline of current challenges and make recommendations on future research directions to practically realize and commercialize the technology for everyday usage

HEAR: Approach for Heartbeat Monitoring with Body Movement Compensation by IR-UWB Radar

Further applications of impulse radio ultra-wideband radar in mobile health are hindered by the difficulty in extracting such vital signals as heartbeats from moving targets. Although the empirical mode decomposition based method is applied in recovering waveforms of heartbeats and estimating heart rates, the instantaneous heart rate is not achievable. This paper proposes a Heartbeat Estimation And Recovery (HEAR) approach to expand the application to mobile scenarios and extract instantaneous heartbeats. Firstly, the HEAR approach acquires vital signals by mapping maximum echo amplitudes to the fast time delay and compensating large body movements. Secondly, HEAR adopts the variational nonlinear chirp mode decomposition in extracting instantaneous frequencies of heartbeats. Thirdly, HEAR extends the clutter removal method based on the wavelet decomposition with a two-parameter exponential threshold. Compared to heart rates simultaneously collected by electrocardiograms (ECG), HEAR achieves a minimum error rate 4.6% in moving state and 2.25% in resting state. The Bland–Altman analysis verifies the consistency of beat-to-beat intervals in ECG and extracted heartbeat signals with the mean deviation smaller than 0.1 s. It indicates that HEAR is practical in offering clinical diagnoses such as the heart rate variability analysis in mobile monitoring

Multiple Person Localization Based on Their Vital Sign Detection Using UWB Sensor

In the past period, great efforts have been made to develop methods for through an obstacle detection of human vital signs such as breathing or heart beating. For that purpose, ultra-wideband (UWB) radars operating in the frequency band DC-5 GHz can be used as a proper tool. The basic principle of respiratory motion detection consists in the identification of radar signal components possessing a significant power in the frequency band 0.2–0.7 Hz (frequency band of human respiratory rate) corresponding to a constant bistatic range between the target and radar. To tackle the task of detecting respiratory motion, a variety of methods have been developed. However, the problem of person localization based on his or her respiratory motion detection has not been studied deeply. In order to fill this gap, an approach for multiple person localization based on the detection of their respiratory motion will be introduced in this chapter

Human Breathing Rate Estimation from Radar Returns Using Harmonically Related Filters

Radar-based noncontact sensing of life sign signals is often used in safety and rescue missions during disasters such as earthquakes and avalanches and for home care applications. The radar returns obtained from a human target contain the breathing frequency along with its strong higher harmonics depending on the target’s posture. As a consequence, well understood, computationally efficient, and the most popular traditional FFT-based estimators that rely only on the strongest peak for estimates of breathing rates may be inaccurate. The paper proposes a solution for correcting the estimation errors of such single peak-based algorithms. The proposed method is based on using harmonically related comb filters over a set of all possible breathing frequencies. The method is tested on three subjects for different postures, for different distances between the radar and the subject, and for two different radar platforms: PN-UWB and phase modulated-CW (PM-CW) radars. Simplified algorithms more suitable for real-time implementation have also been proposed and compared using accuracy and computational complexity. The proposed breathing rate estimation algorithms provide a reduction of about 81% and 80% in the mean absolute error of breathing rates in comparison to the traditional FFT-based methods using strongest peak detection, for PN-UWB and PM-CW radars, respectively

Doppler Radar-Based Non-Contact Health Monitoring for Obstructive Sleep Apnea Diagnosis: A Comprehensive Review

Today’s rapid growth of elderly populations and aging problems coupled with the prevalence of obstructive sleep apnea (OSA) and other health related issues have affected many aspects of society. This has led to high demands for a more robust healthcare monitoring, diagnosing and treatments facilities. In particular to Sleep Medicine, sleep has a key role to play in both physical and mental health. The quality and duration of sleep have a direct and significant impact on people’s learning, memory, metabolism, weight, safety, mood, cardio-vascular health, diseases, and immune system function. The gold-standard for OSA diagnosis is the overnight sleep monitoring system using polysomnography (PSG). However, despite the quality and reliability of the PSG system, it is not well suited for long-term continuous usage due to limited mobility as well as causing possible irritation, distress, and discomfort to patients during the monitoring process. These limitations have led to stronger demands for non-contact sleep monitoring systems. The aim of this paper is to provide a comprehensive review of the current state of non-contact Doppler radar sleep monitoring technology and provide an outline of current challenges and make recommendations on future research directions to practically realize and commercialize the technology for everyday usage.</jats:p

Vital Signs Monitoring Based On UWB Radar

Contactless detection of human vital sign using radar sensors appears to be a promising technology which integrates communication, biomedicine, computer science etc. The radar-based vital sign detection has been actively investigated in the past decade. Due to the advantages such as wide bandwidth, high resolution, small and portable size etc., ultra-wideband (UWB) radar has received a great deal of attention in the health care field. In this thesis, an X4 series UWB radar developed by Xethru Company is adopted to detect human breathing signals through the radar echo reflected by the chest wall movement caused by breath and heartbeat. The emphasis is placed on the estimation of breathing and heart rate based on several signal processing algorithms. Firstly, the research trend of vital sign detection using radar technology is reviewed, based on which the advantages of contactless detection and UWB radar-based technology are highlighted. Then theoretical basis and core algorithms of radar signals detection are presented. Meanwhile, the detection system based on Xethru UWB radar is introduced. Next, several preprocessing methods including SVD-based clutter and noise removal algorithms, the largest variance-based target detection method, and the autocorrelation-based breathing-like signal identification method are investigated, to extract the significant component containing the vital signs from the received raw radar echo signal. Then the thesis investigates four time-frequency analysis algorithms (fast Fourier transform + band-pass filter (FFT+BPF), empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD) and variational mode decomposition (VMD) and compare their performances in estimating breathing rate (BR) and heart rate (HR) in different application scenarios. A python-based vital signs detection system is designed to implement the above-mentioned preprocessing and BR and HR estimation algorithms, based on which a large number of single target experiments are undertaken to evaluate the four estimation algorithms. Specifically, the single target experiments are divided into simple setup and challenging setup. In the simple setup, testees face to radar and keep normal breathing in an almost stationary posture, while in the challenging setup, the testee is allowed to do more actions, such as site sitting, changing the breathing frequency, deep hold the breathing. It is shown that the FFT+BPF algorithm gives the highest accuracy and the fastest calculation speed under the simple setup, while in a challenging setup, the VMD algorithm has the highest accuracy and the widest applicability. Finally, double targets breathing signal detection at different distances to the radar are undertaken, aiming to observe whether the breathing signals of two targets will interfere with each other. We found that when two objects are not located at the same distance to the radar, the object closer to the radar will not affect the breath detection of the object far from the radar. When the two targets are located at the same distance, the 'Shading effect' appears in the two breathing signals and only VMD algorithm can separate the breathing signals of the targets

Doppler Radar Techniques for Distinct Respiratory Pattern Recognition and Subject Identification.

Ph.D. Thesis. University of Hawaiʻi at Mānoa 2017

AN An ica-ensemble learning approach for prediction of uwb nlos signals data classification

Trapped human detection in search and rescue (SAR) scenarios poses a significant challenge in pervasive computing. This study addresses this issue by leveraging machine learning techniques, given their high accuracy. However, accurate identification of trapped individuals is hindered by the curse of dimensionality and noisy data. Particularly in non-line-of-sight (NLOS) situations during catastrophic events, the curse of dimensionality may lead to blind spots due to noise and uncorrelated values in detections. This research focuses on harmonizing information through wireless communication and identifying individuals in NLOS scenarios using ultra-wideband (UWB) radar signals. Employing independent component analysis (ICA) for feature extraction, the study evaluates classification performance using ensemble algorithms on both static and dynamic datasets. The experimental results demonstrate categorization accuracies of 88.37% for static data and 87.20% for dynamic data, highlighting the effectiveness of the proposed approach. Finally, this work can help scientists and engineers make instant decisions during SAR operations.Comment: 20 pages,4 figures, 1 algorithm and 3 table

Non-Contact Human Motion Sensing Using Radar Techniques

Human motion analysis has recently gained a lot of interest in the research community due to its widespread applications. A full understanding of normal motion from human limb joint trajectory tracking could be essential to develop and establish a scientific basis for correcting any abnormalities. Technology to analyze human motion has significantly advanced in the last few years. However, there is a need to develop a non-invasive, cost effective gait analysis system that can be functional indoors or outdoors 24/7 without hindering the normal daily activities for the subjects being monitored or invading their privacy. Out of the various methods for human gait analysis, radar technique is a non-invasive method, and can be carried out remotely. For one subject monitoring, single tone radars can be utilized for motion capturing of a single target, while ultra-wideband radars can be used for multi-subject tracking. But there are still some challenges that need to be overcome for utilizing radars for motion analysis, such as sophisticated signal processing requirements, sensitivity to noise, and hardware imperfections. The goal of this research is to overcome these challenges and realize a non-contact gait analysis system capable of extracting different organ trajectories (like the torso, hands and legs) from a complex human motion such as walking. The implemented system can be hugely beneficial for applications such as treating patients with joint problems, athlete performance analysis, motion classification, and so on

Bezkontaktní měření dechové frekvence s využitím UWB radaru

his master thesis investigates the possibility of remotely measuring respiratory rate during daily life physical non-stationary activity using an IR UWB radar system. The radar signals were processed using Independent Component Analysis (ICA) and Empirical Wavelet Transform (EWT) methods to extract respiratory rate information. The aim was to determine whether the combination of the IR UWB radar system and signal processing techniques can provide an accurate estimate of respiratory rate wirelessly. The outcome of this study suggests that the proposed approach has potential for effective and non-contact monitoring of respiratory rate. The algorithm was evaluated against respiratory rate data obtained through a resistance chest belt during the experiments, and achieved a p-value of the Pearson correlation coefficient analysis of 0.94 and a mean error (ME) value of -0.41, as indicated by the results of the Bland-Altman plot.Tato diplomová práce se zaměřuje na možnost bezkontaktního měření dechové frekvence v průběhu každodenní fyzické aktivity pomocí radarového systému. Signály zachycené radarem byly zpracovány pomocí metod analýzy nezávislých komponent (ICA) a empirické vlnové transformace (EWT) pro extrakci dechové frekvence. Cílem bylo ověřit, zda kombinace IR-UWB radaru a navrženého algoritmu zpracování signálu může poskytnout přesný odhad dechové frekvence bezkontaktně. Výsledky této studie naznačují, že navrhovaný přístup má potenciál pro efektivní a bezkontaktní monitorování dechové frekvence při volném pohybu sledovaných osob. Pro ověření algoritmu bylo navržen a realizován experiment v obytné laboratoři. Algoritmus byl porovnán s daty o dechové frekvenci získanými pomocí odporového hrudního pásu během experimentů a dosáhl hodnoty Pearsonova korelačního koeficientu 0,94 a střední chyby (ME) -0,41, což ukazuje Bland-Altmanův graf.450 - Katedra kybernetiky a biomedicínského inženýrstvívýborn