Self-adjustable domain adaptation in personalized ECG monitoring integrated with IR-UWB radar

To enhance electrocardiogram (ECG) monitoring systems in personalized detections, deep neural networks (DNNs) are applied to overcome individual differences by periodical retraining. As introduced previously [4], DNNs relieve individual differences by fusing ECG with impulse radio ultra-wide band (IR-UWB) radar. However, such DNN-based ECG monitoring system tends to overfit into personal small datasets and is difficult to generalize to newly collected unlabeled data. This paper proposes a self-adjustable domain adaptation (SADA) strategy to prevent from overfitting and exploit unlabeled data. Firstly, this paper enlarges the database of ECG and radar data with actual records acquired from 28 testers and expanded by the data augmentation. Secondly, to utilize unlabeled data, SADA combines self organizing maps with the transfer learning in predicting labels. Thirdly, SADA integrates the one-class classification with domain adaptation algorithms to reduce overfitting. Based on our enlarged database and standard databases, a large dataset of 73200 records and a small one of 1849 records are built up to verify our proposal. Results show SADA\u27s effectiveness in predicting labels and increments in the sensitivity of DNNs by 14.4% compared with existing domain adaptation algorithms

Non-Invasive Driver Drowsiness Detection System.

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

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

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

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

Respiratory Rate Monitoring in Clinical Environments with a Contactless Ultra-Wideband Impulse Radar-based Sensor System

Respiratory rate is an extremely important but poorly monitored vital sign for medical conditions. Current modalities for respiratory monitoring are suboptimal. This paper presents a proof of concept of a new algorithm using a contactless ultra-wideband (UWB) impulse radar-based sensor to detect respiratory rate in both a laboratory setting and in a two-subject case study in the Emergency Department. This novel approach has shown correlation with manual respiratory rate in the laboratory setting and shows promise in Emergency Department subjects. In order to improve respiratory rate monitoring, the UWB technology is also able to localize subject movement throughout the room. This technology has potential for utilization both in and out of the hospital environments to improve monitoring and to prevent morbidity and mortality from a variety of medical conditions associated with changes in respiratory rate

Portable UWB RADAR Sensing System for Transforming Subtle Chest Movement into Actionable Micro-Doppler Signatures to Extract Respiratory Rate Exploiting ResNet Algorithm

Contactless or non-invasive technology for the monitoring of anomalies in an inconspicuous and distant environment has immense significance in health-related applications, in particular COVID-19 symptoms detection, diagnosis, and monitoring. Contactless methods are crucial specifically during the COVID-19 epidemic as they require the least amount of involvement from infected individuals as well as healthcare personnel. According to recent medical research studies regarding coronavirus, individuals infected with novel COVID-19-Delta variant undergo elevated respiratory rates due to extensive infection in the lungs. This appalling situation demands constant real-time monitoring of respiratory patterns, which can help in avoiding any pernicious circumstances. In this paper, an Ultra-Wideband RADAR sensor “XeThru X4M200” is exploited to capture vital respiratory patterns. In the low and high frequency band, X4M200 operates within the 6.0-8.5 GHz and 7.25-10.20 GHz band, respectively. The experimentation is conducted on six distinct individuals to replicate a realistic scenario of irregular respiratory rates. The data is obtained in the form of spectrograms by carrying out normal (eupnea) and abnormal (tachypnea) respiratory. The collected spectrogram data is trained, validated, and tested using a cutting-edge deep learning technique called Residual Neural Network or ResNet. The trained ResNet model’s performance is assessed using the confusion matrix, precision, recall, F1-score, and classification accuracy. The unordinary skip connection process of the deep ResNet algorithm significantly reduces the underfitting and overfitting problem, resulting in a classification accuracy rate of up to 90%

Bio-Radar Applications for Remote Vital Signs Monitoring

Nowadays, most vital signs monitoring techniques used in a medical context and/or daily life routines require direct contact with skin, which can become uncomfortable or even impractical to be used regularly. Radar technology has been appointed as one of the most promising contactless tools to overcome these hurdles. However, there is a lack of studies that cover a comprehensive assessment of this technology when applied in real-world environments. This dissertation aims to study radar technology for remote vital signs monitoring, more specifically, in respiratory and heartbeat sensing. Two off-the-shelf radars, based on impulse radio ultra-wideband and frequency modu lated continuous wave technology, were customized to be used in a small proof of concept experiment with 10 healthy participants. Each subject was monitored with both radars at three different distances for two distinct conditions: breathing and voluntary apnea. Signals processing algorithms were developed to detect and estimate respiratory and heartbeat parameters, assessed using qualitative and quantitative methods. Concerning respiration, a minimum error of 1.6% was found when radar respiratory peaks signals were directly compared with their reference, whereas a minimum mean absolute error of 0.3 RPM was obtained for the respiration rate. Concerning heartbeats, their expression in radar signals was not as clear as the respiration ones, however a minimum mean absolute error of 1.8 BPM for heartbeat was achieved after applying a novel selective algorithm developed to validate if heart rate value was estimated with reliability. The results proved the potential for radars to be used in respiratory and heartbeat contactless sensing, showing that the employed methods can be already used in some mo tionless situations. Notwithstanding, further work is required to improve the developed algorithms in order to obtain more robust and accurate systems.Atualmente, a maioria das técnicas usadas para a monitorização de sinais vitais em contexto médicos e/ou diário requer contacto direto com a pele, o que poderá tornar-se incómodo ou até mesmo inviável em certas situações. A tecnologia radar tem vindo a ser apontada como uma das mais promissoras ferramentas para medição de sinais vitais à distância e sem contacto. Todavia, são necessários mais estudos que permitam avaliar esta tecnologia quando aplicada a situações mais reais. Esta dissertação tem como objetivo o estudo da tecnologia radar aplicada no contexto de medição remota de sinais vitais, mais concretamente, na medição de atividade respiratória e cardíaca. Dois aparelhos radar, baseados em tecnologia banda ultra larga por rádio de impulso e em tecnologia de onda continua modulada por frequência, foram configurados e usados numa prova de conceito com 10 participantes. Cada sujeito foi monitorizado com cada um dos radar em duas situações distintas: respirando e em apneia voluntária. Algorit mos de processamento de sinal foram desenvolvidos para detetar e estimar parâmetros respiratórios e cardíacos, avaliados através de métodos qualitativos e quantitativos. Em relação à respiração, o menor erro obtido foi de 1,6% quando os sinais de radar respiratórios foram comparados diretamente com os sinais de referência, enquanto que, um erro médio absoluto mínimo de 0,3 RPM foi obtido para a estimação da frequência respiratória via radar. A expressão cardíaca nos sinais radar não se revelou tão evidente como a respiratória, no entanto, um erro médio absoluto mínimo de 1,8 BPM foi obtido para a estimação da frequência cardíaca após a aplicação de um novo algoritmo seletivo, desenvolvido para validar a confiança dos valores obtidos. Os resultados obtidos provaram o potencial do uso de radares na medição de atividade respiratória e cardíaca sem contacto, sendo esta tecnologia viável de ser implementada em situações onde não existe muito movimento. Não obstante, os algoritmos desenvolvidos devem ser aperfeiçoados no futuro de forma a obter sistemas mais robustos e precisos