184 research outputs found

    UWB radar for non-contact heart rate variability monitoring and mental state classification.

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    Heart rate variability (HRV), as measured by ultra-wideband (UWB) radar, enables contactless monitoring of physiological functioning in the human body. In the current study, we verified the reliability of HRV extraction from radar data, under limited transmitter power. In addition, we conducted a feasibility study of mental state classification from HRV data, measured using radar. Specifically, arctangent demodulation with calibration and low rank approximation have been used for radar signal pre-processing. An adaptive continuous wavelet filter and moving average filter were utilized for HRV extraction. For the mental state classification task, performance of support vector machine, k-nearest neighbors and random forest classifiers have been compared. The developed system has been validated on human participants, with 10 participants for HRV extraction, and three participants for the proof-of-concept mental state classification study. The results of HRV extraction demonstrate the reliability of time-domain parameter extraction from radar data. However, frequency-domain HRV parameters proved to be unreliable under low SNR. The best average overall mental state classification accuracy achieved was 82.34%, which has important implications for the feasibility of mental health monitoring using UWB radar

    Noncontact Measurement of Autonomic Nervous System Activities Based on Heart Rate Variability Using Ultra-Wideband Array Radar

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    The noncontact measurement of vital signs using ultra-wideband radar has been attracting increasing attention because it can unobtrusively provide information about the physical and mental condition of people. In particular, the continuous measurement of a person's time-varying instantaneous heart rate can estimate the activity level of the autonomic nervous system without the person wearing any sensors. Continuous heart rate measurement using radar is, however, a difficult task because accuracy is compromised by numerous factors, such as the posture and motion of the target person. In this study, we introduce techniques for increasing the accuracy and reliability of the noncontact measurement of heart rate variability. We demonstrate the performance of the proposed techniques by applying them to radar measurement data from a sleeping person, and we also compare its accuracy with electrocardiogram data

    Respiration-Based COPD Detection Using UWB Radar Incorporation with Machine Learning

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    COPD is a progressive disease that may lead to death if not diagnosed and treated at an early stage. The examination of vital signs such as respiration rate is a promising approach for the detection of COPD. However, simultaneous consideration of the demographic and medical characteristics of patients is very important for better results. The objective of this research is to investigate the capability of UWB radar as a non-invasive approach to discriminate COPD patients from healthy subjects. The non-invasive approach is beneficial in pandemics such as the ongoing COVID-19 pandemic, where a safe distance between people needs to be maintained. The raw data are collected in a real environment (a hospital) non-invasively from a distance of 1.5 m. Respiration data are then extracted from the collected raw data using signal processing techniques. It was observed that the respiration rate of COPD patients alone is not enough for COPD patient detection. However, incorporating additional features such as age, gender, and smoking history with the respiration rate lead to robust performance. Different machine-learning classifiers, including Naïve Bayes, support vector machine, random forest, k nearest neighbor (KNN), Adaboost, and two deep-learning models—a convolutional neural network and a long short-term memory (LSTM) network—were utilized for COPD detection. Experimental results indicate that LSTM outperforms all employed models and obtained 93% accuracy. Performance comparison with existing studies corroborates the superior performance of the proposed approach

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

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    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

    Non-Invasive Driver Drowsiness Detection System.

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    Drowsiness when in command of a vehicle leads to a decline in cognitive performance that affects driver behavior, potentially causing accidents. Drowsiness-related road accidents lead to severe trauma, economic consequences, impact on others, physical injury and/or even death. Real-time and accurate driver drowsiness detection and warnings systems are necessary schemes to reduce tiredness-related driving accident rates. The research presented here aims at the classification of drowsy and non-drowsy driver states based on respiration rate detection by non-invasive, non-touch, impulsive radio ultra-wideband (IR-UWB) radar. Chest movements of 40 subjects were acquired for 5 m using a lab-placed IR-UWB radar system, and respiration per minute was extracted from the resulting signals. A structured dataset was obtained comprising respiration per minute, age and label (drowsy/non-drowsy). Different machine learning models, namely, Support Vector Machine, Decision Tree, Logistic regression, Gradient Boosting Machine, Extra Tree Classifier and Multilayer Perceptron were trained on the dataset, amongst which the Support Vector Machine shows the best accuracy of 87%. This research provides a ground truth for verification and assessment of UWB to be used effectively for driver drowsiness detection based on respiration

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

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    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

    Unobtrusive cot side sleep stage classification in preterm infants using ultra-wideband radar

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    Background: Sleep is an important driver of development in infants born preterm. However, continuous unobtrusive sleep monitoring of infants in the neonatal intensive care unit (NICU) is challenging.Objective: To assess the feasibility of ultra-wideband (UWB) radar for sleep stage classification in preterm infants admitted to the NICU.Methods: Active and quiet sleep were visually assessed using video recordings in 10 preterm infants (recorded between 29 and 34 weeks of postmenstrual age) admitted to the NICU. UWB radar recorded all infant's motions during the video recordings. From the baseband data measured with the UWB radar, a total of 48 features were calculated. All features were related to body and breathing movements. Six machine learning classifiers were compared regarding their ability to reliably classify active and quiet sleep using these raw signals.Results: The adaptive boosting (AdaBoost) classifier achieved the highest balanced accuracy (81%) over a 10-fold cross-validation, with an area under the curve of receiver operating characteristics (AUC-ROC) of 0.82.Conclusions: The UWB radar data, using the AdaBoost classifier, is a promising method for non-obtrusive sleep stage assessment in very preterm infants admitted to the NICU

    A systematic review of physiological signals based driver drowsiness detection systems.

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    Driving a vehicle is a complex, multidimensional, and potentially risky activity demanding full mobilization and utilization of physiological and cognitive abilities. Drowsiness, often caused by stress, fatigue, and illness declines cognitive capabilities that affect drivers' capability and cause many accidents. Drowsiness-related road accidents are associated with trauma, physical injuries, and fatalities, and often accompany economic loss. Drowsy-related crashes are most common in young people and night shift workers. Real-time and accurate driver drowsiness detection is necessary to bring down the drowsy driving accident rate. Many researchers endeavored for systems to detect drowsiness using different features related to vehicles, and drivers' behavior, as well as, physiological measures. Keeping in view the rising trend in the use of physiological measures, this study presents a comprehensive and systematic review of the recent techniques to detect driver drowsiness using physiological signals. Different sensors augmented with machine learning are utilized which subsequently yield better results. These techniques are analyzed with respect to several aspects such as data collection sensor, environment consideration like controlled or dynamic, experimental set up like real traffic or driving simulators, etc. Similarly, by investigating the type of sensors involved in experiments, this study discusses the advantages and disadvantages of existing studies and points out the research gaps. Perceptions and conceptions are made to provide future research directions for drowsiness detection techniques based on physiological signals. [Abstract copyright: © The Author(s), under exclusive licence to Springer Nature B.V. 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

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

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    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

    Bio-Radar: sistema de aquisição de sinais vitais sem contacto

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    The Bio-Radar system is capable to measure vital signs accurately, namely the respiratory and cardiac signal, using electromagnetic waves. In this way, it is possible to monitor subjects remotely and comfortably for long periods of time. This system is based on the micro-Doppler effect, which relates the received signal phase variation with the distance change between the subject chest-wall and the radar antennas, which occurs due to the cardiopulmonary function. Considering the variety of applications where this system can be used, it is required to evaluate its performance when applied to real context scenarios and thus demonstrate the advantages that bioradar systems can bring to the general population. In this work, a bio-radar prototype was developed in order to verify the viability to be integrated in specific applications, using robust and low profile solutions that equally guarantee the general system performance while addressing the market needs. Considering these two perspectives to be improved, different level solutions were developed. On the hardware side, textile antennas were developed to be embedded in a car seat upholstery, thus reaching a low profile solution and easy to include in the industrialization process. Real context scenarios imply long-term monitoring periods, where involuntary body motion can occur producing high amplitude signals that overshadow the vital signs. Non-controlled monitoring environments might also produce time varying parasitic reflections that have a direct impact in the signal. Additionally, the subject's physical stature and posture during the monitoring period can have a different impact in the signals quality. Therefore, signal processing algorithms were developed to be robust to low quality signals and non-static scenarios. On the other hand, the bio-radar potential can also be maximized if the acquired signals are used pertinently to help identify the subject's psychophysiological state enabling one to act accordingly. The random body motion until now has been seen as a noisy source, however it can also provide useful information regarding subject's state. In this sense, the acquired vital signs as well as other body motions were used in machine learning algorithms with the goal to identify the subject's emotions and thus verify if the remotely acquired vital signs can also provide useful information.O sistema Bio-Radar permite medir sinais vitais com precisão, nomeadamente o sinal respiratório e cardíaco, utilizando ondas eletromagnéticas para esse fim. Desta forma, é possível monitorizar sujeitos de forma remota e confortável durante longos períodos de tempo. Este sistema é baseado no efeito de micro-Doppler, que relaciona a variação de fase do sinal recebido com a alteração da distância entre as antenas do radar e a caixa torácica do sujeito, que ocorre durante a função cardiopulmonar. Considerando a variedade de aplicações onde este sistema pode ser utilizado, é necessário avaliar o seu desempenho quando aplicado em contextos reais e assim demonstrar as vantagens que os sistemas bio-radar podem trazer à população geral. Neste trabalho, foi desenvolvido um protótipo do bio radar com o objetivo de verificar a viabilidade de integrar estes sistemas em aplicações específicas, utilizando soluções robustas e discretas que garantam igualmente o seu bom desempenho, indo simultaneamente de encontro às necessidades do mercado. Considerando estas duas perspetivas em que o sistema pode ser melhorado, foram desenvolvidas soluções de diferentes níveis. Do ponto de vista de hardware, foram desenvolvidas antenas têxteis para serem integradas no estofo de um banco automóvel, alcançando uma solução discreta e fácil de incluir num processo de industrialização. Contextos reais de aplicação implicam períodos de monitorização longos, onde podem ocorrer movimentos corporais involuntários que produzem sinais de elevada amplitude que se sobrepõem aos sinais vitais. Ambientes de monitorização não controlados podem produzir reflexões parasitas variantes no tempo que têm impacto direto no sinal. Adicionalmente, a estrutura física do sujeito e a sua postura durante o período de monitorização podem ter impactos diferentes na qualidade dos sinais. Desta forma, foram desenvolvidos algoritmos de processamento de sinal robustos a sinais de baixa qualidade e a cenários não estáticos. Por outro lado, o potencial do bio radar pode também ser maximizado se os sinais adquiridos forem pertinentemente utilizados de forma a ajudar a identificar o estado psicofisiológico do sujeito, permitindo mais tarde agir em conformidade. O movimento corporal aleatório que foi até agora visto como uma fonte de ruído, pode no entanto também fornecer informação útil sobre o estado do sujeito. Neste sentido, os sinais vitais e outros movimentos corporais adquiridos foram utilizados em algoritmos de aprendizagem automática com o objetivo de identificar as emoções do sujeito e assim verificar que sinais vitais adquiridos remotamente podem também conter informação útil.Programa Doutoral em Engenharia Eletrotécnic
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