Sensors for Vital Signs Monitoring

Sensor technology for monitoring vital signs is an important topic for various service applications, such as entertainment and personalization platforms and Internet of Things (IoT) systems, as well as traditional medical purposes, such as disease indication judgments and predictions. Vital signs for monitoring include respiration and heart rates, body temperature, blood pressure, oxygen saturation, electrocardiogram, blood glucose concentration, brain waves, etc. Gait and walking length can also be regarded as vital signs because they can indirectly indicate human activity and status. Sensing technologies include contact sensors such as electrocardiogram (ECG), electroencephalogram (EEG), photoplethysmogram (PPG), non-contact sensors such as ballistocardiography (BCG), and invasive/non-invasive sensors for diagnoses of variations in blood characteristics or body fluids. Radar, vision, and infrared sensors can also be useful technologies for detecting vital signs from the movement of humans or organs. Signal processing, extraction, and analysis techniques are important in industrial applications along with hardware implementation techniques. Battery management and wireless power transmission technologies, the design and optimization of low-power circuits, and systems for continuous monitoring and data collection/transmission should also be considered with sensor technologies. In addition, machine-learning-based diagnostic technology can be used for extracting meaningful information from continuous monitoring data

NON-CONTACT TECHNIQUES FOR HUMAN VITAL SIGN DETECTION AND GAIT ANALYSIS

Human vital signs including respiratory rate, heart rate, oxygen saturation, blood pressure, and body temperature are important physiological parameters that are used to track and monitor human health condition. Another important biological parameter of human health is human gait. Human vital sign detection and gait investigations have been attracted many scientists and practitioners in various fields such as sport medicine, geriatric medicine, bio-mechanic and bio-medical engineering and has many biological and medical applications such as diagnosis of health issues and abnormalities, elderly care and health monitoring, athlete performance analysis, and treatment of joint problems. Thoroughly tracking and understanding the normal motion of human limb joints can help to accurately monitor human subjects or patients over time to provide early flags of possible complications in order to aid in a proper diagnosis and development of future comprehensive treatment plans. With the spread of COVID-19 around the world, it has been getting more important than ever to employ technology that enables us to detect human vital signs in a non-contact way and helps protect both patients and healthcare providers from potentially life-threatening viruses, and have the potential to also provide a convenient way to monitor people health condition, remotely. A popular technique to extract biological parameters from a distance is to use cameras. Radar systems are another attractive solution for non-contact human vital signs monitoring and gait investigation that track and monitor these biological parameters without invading people privacy. The goal of this research is to develop non-contact methods that is capable of extracting human vital sign parameters and gait features accurately. To do that, in this work, optical systems including cameras and proper filters have been developed to extract human respiratory rate, heart rate, and oxygen saturation. Feasibility of blood pressure extraction using the developed optical technique has been investigated, too. Moreover, a wideband and low-cost radar system has been implemented to detect single or multiple human subject’s respiration and heart rate in dark or from behind the wall. The performance of the implemented radar system has been enhanced and it has been utilized for non-contact human gait analysis. Along with the hardware, advanced signal processing schemes have been enhanced and applied to the data collected using the aforementioned radar system. The data processing algorithms have been extended for multi-subject scenarios with high accuracy for both human vital sign detection and gait analysis. In addition, different configurations of this and high-performance radar system including mono-static and MIMO have been designed and implemented with great success. Many sets of exhaustive experiments have been conducted using different human subjects and various situations and accurate reference sensors have been used to validate the performance of the developed systems and algorithms

Contact-free Measurement of Heart Rate Variability via a Microwave Sensor

Measures of heart rate variability (HRV) are widely used to assess autonomic nervous system (ANS) function. HRV can be recorded via electrocardiography (ECG), which is both non-invasive and widely available. However, ECG needs three electrodes touching the body of the subjects, which makes them feel nervous and uncomfortable, thus potentially affecting the recording. Contact-free detection of the heartbeat via a microwave sensor constitutes another means of determining the timing of cardiac cycles by continuous monitoring of mechanical contraction of the heart. This technique can measure the heartbeat without any electrodes touching human body and penetrate the clothes at some distances, which in some instances may prove a practical basis for HRV analysis. Comparison of 5-minute recordings demonstrated that there were no significant differences in the temporal, frequency domains and in non-linear dynamic analysis of HRV measures derived from heartbeat and ECG, which suggested this technique may prove a practical alternative to ECG for HRV analysis

Physiological Parameter Sensing with Wearable Devices and Non-Contact Dopper Radar.

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

Assessing the Viability of Complex Electrical Impedance Tomography (EIT) with a Spatially Distributed Sensor Array for Imaging of River Bed Morphology: a Proof of Concept (Study)

This report was produced as part of a NERC funded ‘Connect A’ project to establish a new collaborative partnership between the University of Worcester (UW) and Q-par Angus Ltd. The project aim was to assess the potential of using complex Electrical Impedance Tomography (EIT) to image river bed morphology. An assessment of the viability of sensors inserted vertically into the channel margins to provide real-time or near real-time monitoring of bed morphology is reported. Funding has enabled UW to carry out a literature review of the use of EIT and existing methods used for river bed surveys, and outline the requirements of potential end-users. Q-par Angus has led technical developments and assessed the viability of EIT for this purpose. EIT is one of a suite of tomographic imaging techniques and has already been used as an imaging tool for medical analysis, industrial processing and geophysical site survey work. The method uses electrodes placed on the margins or boundary of the entity being imaged, and a current is applied to some and measured on the remaining ones. Tomographic reconstruction uses algorithms to estimate the distribution of conductivity within the object and produce an image of this distribution from impedance measurements. The advantages of the use of EIT lie with the inherent simplicity, low cost and portability of the hardware, the high speed of data acquisition for real-time or near real-time monitoring, robust sensors, and the object being monitored is done so in a non-invasive manner. The need for sophisticated image reconstruction algorithms, and providing images with adequate spatial resolution are key challenges. A literature review of the use of EIT suggests that to date, despite its many other applications, to the best of our knowledge only one study has utilised EIT for river survey work (Sambuelli et al 2002). The Sambuelli (2002) study supported the notion that EIT may provide an innovative way of describing river bed morphology in a cost effective way. However this study used an invasive sensor array, and therefore the potential for using EIT in a non-invasive way in a river environment is still to be tested. A review of existing methods to monitor river bed morphology indicates that a plethora of techniques have been applied by a range of disciplines including fluvial geomorphology, ecology and engineering. However, none provide non-invasive, low costs assessments in real-time or near real-time. Therefore, EIT has the potential to meet the requirements of end users that no existing technique can accomplish. Work led by Q-par Angus Ltd. has assessed the technical requirements of the proposed approach, including probe design and deployment, sensor array parameters, data acquisition, image reconstruction and test procedure. Consequently, the success of this collaboration, literature review, identification of the proposed approach and potential applications of this technique have encouraged the authors to seek further funding to test, develop and market this approach through the development of a new environmental sensor

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

Laser doppler vibrometry for cardiovascular monitoring and photoacoustic imaging

Nowadays, techniques for health monitoring mainly require physical contact with patients, which is not always ideal. Non-contact health monitoring has become an important research topic in the last decades. The non-contact detection of a patient's health condition represents a beneficial tool in different biomedical fields. Examples can be found in intensive care, home health care, the nursing of the elderly, the monitoring of physical efforts, and in human-machine interactions. Cardiovascular diseases (CV) are one of the most spread causes of death in developed countries. Their monitoring techniques involve physical contact with patients. A non-contact technique for cardiovascular monitoring could overcome problems related to the contact with the patient such as skin lesions. It could also expand the availability of monitoring to those cases where contact is not possible or should be avoided to reduce the exposure of medical personnel to biochemical hazard conditions.Several research groups have investigated different techniques for non-contact monitoring of health; among them, the laser Doppler Vibrometry (LDVy) has one of the highest accuracies and signal to noise ratios for cardiorespiratory signals detection. Moreover, the simplicity of data processing, the long-distance measurement range, and the high bandwidth make the laser Doppler vibrometer (LDV) suitable for daily measurements. LDVy is an interferometric technique employed for the measurements of displacement or velocity signals in various fields. In particular, it is deployed in the biomedical field for the extraction of several cardiovascular parameters, such as the PR-time. Generally, the extraction of these parameters requires ideal measuring conditions (measuring spot and laser direction), which are not realistic for daily monitoring in non-laboratory conditions, and especially in tracking applications. The first scientific hypothesis of this work is that the PR-time detected with LDV has an acceptable uncertainty for a realistic variety of measurement spot positions and angles of the incident laser beam. Therefore, I investigated the uncertainty contribution to the detection of the PR-time from LDV signals resulting from the laser beam direction and from the measurement point position; these investigations were carried out with a multipoint laser Doppler vibrometer. The uncertainties were evaluated according to the Guide to the Expression of Uncertainty in Measurement. Successively, the ranges of PR-time values where it is possible to state with 95% certainty that a diagnosis is correct are identified. Normal values of PR-time are included in the range 120 ms -200 ms. For single value measurements with precise alignment the reliable range for the detection of the healthy condition is 146.4 ms -173.6 ms. The detection of CV diseases is reliable for measured values lower than 93.6 ms and greater than 226.4 ms. For mean value measurements with precise alignment the reliable range for the detection of the healthy condition is 126.6 ms -193.4 ms. The detection of CV diseases is reliable for measured values lower than 113.4 ms and greater than 206.6 ms. Therefore, for measured values included in the mentioned ranges, the detection of the PR-time and relative diagnosis with the LDVy in non-laboratory conditions is reliable. The method for the estimation of the uncertainty contribution proposed in this work can be applied to other cardiovascular parameters extracted with the LDVy. Recently, the LDVy was employed for the detection of tumors in tissue-mimic phantoms as a noncontact alternative to the ultrasound sensors employed in photoacoustic imaging (PAI). A non-contact method has considerable advantages for photoacustic imaging, too. Several works present the possibility to perform PAI measurements with LDVy. However, a successful detection of the signals generated by a tumor depends on the metrological characteristics of the LDV, on the properties of the tumor and of the tissue. The conditions under which a tumor is detectable with the laser Doppler vibrometer has not been investigated yet. The second scientific hypothesis of this work is that, under certain conditions, photoacoustic imaging measurements with LDVy are feasible. Therefore, I identified those conditions to determine the detection limits of LDVy for PAI measurements. These limits were deduced by considering the metrological characteristics of a commercial LDV, the dimensions and the position of the tumor in the tissue. I derived a model for the generation and propagation of PA signals and its detection with an LDV. The model was validated by performing experiments on silicone tissue-micking phantoms. The validated model with breast-tissue parameters reveals the limits of tumor detection with LDVy-based PAI. The results show that commercial LDVs can detect tumors with a minimal radius of ≈350 μm reliably if they are located at a maximal depth in tissue of ≈2 cm. Depending on the position of the detection point, the maximal depth can diminish and depending on the absorption characteristics of the tumor, the detection range increases.Heutzutage erfordern Techniken zur Gesundheitsüberwachung hauptsächlich den physischen Kontakt mit dem Patienten, was nicht immer ideal ist. Die berührungslose Gesundheitsüberwachung hat sich in den letzten Jahrzehnten zu einem wichtigen Forschungsthema entwickelt. Die berührungslose Erkennung des Gesundheitszustands eines Patienten stellt ein nützliches Instrument in verschiedenen biomedizinischen Bereichen dar. Beispiele finden sich in der Intensivpflege, der häuslichen Krankenpflege, der Altenpflege, der Überwachung körperlicher Anstrengungen und in der MenschMaschine-Interaktion. Herz-Kreislauf-Erkrankungen sind eine der am weitesten verbreiteten Todesursachen in den Industrieländern. Ihre Überwachungstechniken erfordern einen physischen Kontakt mit den Patienten. Eine berührungslose Technik für die Überwachung von Herz-KreislaufErkrankungen könnte Probleme im Zusammenhang mit dem Kontakt mit dem Patienten, wie z. B. Hautverletzungen, überwinden. Verschiedene Messgeräte wurden für die berührungslose Überwachung der Gesundheit untersucht; unter ihnen hat das Laser-Doppler-Vibrometrer (LDV) eine der höchsten Genauigkeiten und Signal-Rausch-Verhältnisse für die Erkennung kardiorespiratorischer Signale. Darüber hinaus ist das Laser-Doppler-Vibrometer (LDV) aufgrund der einfachen Datenverarbeitung, des großen Messbereichs und der hohen Bandbreite für tägliche Messungen geeignet. LDV ist ein interferometrisches Verfahren, das zur Messung von Weg- oder Geschwindigkeitssignalen in verschiedenen Bereichen eingesetzt wird. Insbesondere wird es im biomedizinischen Bereich für die Extraktion verschiedener kardiovaskulärer Parameter, wie z. B. der PR-Zeit, eingesetzt. Im Allgemeinen erfordert die Extraktion dieser Parameter ideale Messbedingungen (Messfleck und Laserrichtung), die für die tägliche Überwachung unter Nicht-Laborbedingungen und insbesondere für TrackingAnwendungen nicht realistisch sind. Die erste wissenschaftliche Hypothese dieser Arbeit ist, dass die mit dem LDV ermittelte PR-Zeit eine akzeptable Unsicherheit für eine realistische Vielzahl von Messpunktpositionen und Winkeln des einfallenden Laserstrahls aufweist. Daher wurde der Unsicherheitsbeitrag zur Ermittlung der PR-Zeit aus LDV-Signalen untersucht, der sich aus der Laserstrahlrichtung und der Messpunktposition ergibt; diese Untersuchungen wurden mit einem Mehrpunkt-Laser-Doppler-Vibrometer durchgeführt. Die Unsicherheiten wurden gemäß der Technische Regel ISO/IEC Guide 98-3:2008-09 Messunsicherheit – Teil 3: Leitfaden zur Angabe der Unsicherheit beim Messen bewertet. Nacheinander werden die Bereiche der PR-Zeit-Werte ermittelt, in denen mit 95%iger Sicherheit eine korrekte Diagnose gestellt werden kann. Die in dieser Arbeit vorgeschlagene Methode zur Schätzung des Unsicherheitsbeitrags kann auch auf andere kardiovaskuläre Parameter angewendet werden, die mit dem LDV extrahiert werden. Kürzlich wurde das LDV zur Erkennung von Tumoren in gewebeähnlichen Phantomen als berührungslose Alternative zu den Ultraschallsensoren eingesetzt, die bei der photoakustischen Bildgebung (PAI) verwendet werden. Eine berührungslose Methode hat auch für die photoakustische Bildgebung erhebliche Vorteile. In mehreren Arbeiten wird die Möglichkeit vorgestellt, PAIMessungen mit LDV durchzuführen. Die erfolgreiche Erkennung der von einem Tumor erzeugten Signale hängt jedoch von den messtechnischen Eigenschaften des LDV sowie von den Eigenschaften des Tumors und des Gewebes ab. Die Bedingungen, unter denen ein Tumor mit dem LDV detektierbar ist, wurden bisher nicht untersucht. Die zweite wissenschaftliche Hypothese dieser Arbeit ist, dass unter bestimmten Bedingungen photoakustische Bildgebungsmessungen mit dem LDV möglich sind. Daher wurden diese Bedingungen ermittelt, um die Nachweisgrenzen von LDV für PAI-Messungen zu bestimmen. Diese Grenzen wurden unter Berücksichtigung der messtechnischen Eigenschaften eines handelsüblichen LDV, der Abmessungen und der Position des Tumors im Gewebe abgeleitet. In dieser Arbeit wurde ein Modell für die Erzeugung und Ausbreitung von PA-Signalen und deren Nachweis mit einem LDV abgeleitet. Das Modell wurde durch Experimente an Silikongewebe-Phantomen validiert. Das validierte Modell mit Parametern des Brustgewebes zeigt die Grenzen der Tumorerkennung mit LDV-basierter PAI auf. Die Ergebnisse zeigen, dass kommerzielle LDV Tumore mit einem minimalen Radius von ≈350 μm zuverlässig erkennen können

Noncontact Vital Signs Detection

Human health condition can be accessed by measurement of vital signs, i.e., respiratory rate (RR), heart rate (HR), blood oxygen level, temperature and blood pressure. Due to drawbacks of contact sensors in measurement, non-contact sensors such as imaging photoplethysmogram (IPPG) and Doppler radar system have been proposed for cardiorespiratory rates detection by researchers.The UWB pulse Doppler radars provide high resolution range-time-frequency information. It is bestowed with advantages of low transmitted power, through-wall capabilities, and high resolution in localization. However, the poor signal to noise ratio (SNR) makes it challenging for UWB radar systems to accurately detect the heartbeat of a subject. To solve the problem, phased-methods have been proposed to extract the phase variations in the reflected pulses modulated by human tiny thorax motions. Advance signal processing method, i.e., state space method, can not only be used to enhance SNR of human vital signs detection, but also enable the micro-Doppler trajectories extraction of walking subject from UWB radar data.Stepped Frequency Continuous Wave (SFCW) radar is an alternative technique useful to remotely monitor human subject activities. Compared with UWB pulse radar, it relieves the stress on requirement of high sampling rate analog-to-digital converter (ADC) and possesses higher signal-to-noise-ratio (SNR) in vital signs detection. However, conventional SFCW radar suffers from long data acquisition time to step over many frequencies. To solve this problem, multi-channel SFCW radar has been proposed to step through different frequency bandwidths simultaneously. Compressed sensing (CS) can further reduce the data acquisition time by randomly stepping through 20% of the original frequency steps.In this work, SFCW system is implemented with low cost, off-the-shelf surface mount components to make the radar sensors portable. Experimental results collected from both pulse and SFCW radar systems have been validated with commercial contact sensors and satisfactory results are shown

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

Index to 1984 NASA Tech Briefs, volume 9, numbers 1-4

Short announcements of new technology derived from the R&D activities of NASA are presented. These briefs emphasize information considered likely to be transferrable across industrial, regional, or disciplinary lines and are issued to encourage commercial application. This index for 1984 Tech B Briefs contains abstracts and four indexes: subject, personal author, originating center, and Tech Brief Number. The following areas are covered: electronic components and circuits, electronic systems, physical sciences, materials, life sciences, mechanics, machinery, fabrication technology, and mathematics and information sciences