Unobtrusive Health Monitoring in Private Spaces: The Smart Vehicle

Unobtrusive in-vehicle health monitoring has the potential to use the driving time to perform regular medical check-ups. This work intends to provide a guide to currently proposed sensor systems for in-vehicle monitoring and to answer, in particular, the questions: (1) Which sensors are suitable for in-vehicle data collection? (2) Where should the sensors be placed? (3) Which biosignals or vital signs can be monitored in the vehicle? (4) Which purposes can be supported with the health data? We reviewed retrospective literature systematically and summarized the up-to-date research on leveraging sensor technology for unobtrusive in-vehicle health monitoring. PubMed, IEEE Xplore, and Scopus delivered 959 articles. We firstly screened titles and abstracts for relevance. Thereafter, we assessed the entire articles. Finally, 46 papers were included and analyzed. A guide is provided to the currently proposed sensor systems. Through this guide, potential sensor information can be derived from the biomedical data needed for respective purposes. The suggested locations for the corresponding sensors are also linked. Fifteen types of sensors were found. Driver-centered locations, such as steering wheel, car seat, and windscreen, are frequently used for mounting unobtrusive sensors, through which some typical biosignals like heart rate and respiration rate are measured. To date, most research focuses on sensor technology development, and most application-driven research aims at driving safety. Health-oriented research on the medical use of sensor-derived physiological parameters is still of interest

Detecting Vital Signs with Wearable Wireless Sensors

The emergence of wireless technologies and advancements in on-body sensor design can enable change in the conventional health-care system, replacing it with wearable health-care systems, centred on the individual. Wearable monitoring systems can provide continuous physiological data, as well as better information regarding the general health of individuals. Thus, such vital-sign monitoring systems will reduce health-care costs by disease prevention and enhance the quality of life with disease management. In this paper, recent progress in non-invasive monitoring technologies for chronic disease management is reviewed. In particular, devices and techniques for monitoring blood pressure, blood glucose levels, cardiac activity and respiratory activity are discussed; in addition, on-body propagation issues for multiple sensors are presented

Blind Source Separation for the Processing of Contact-Less Biosignals

(Spatio-temporale) Blind Source Separation (BSS) eignet sich für die Verarbeitung von Multikanal-Messungen im Bereich der kontaktlosen Biosignalerfassung. Ziel der BSS ist dabei die Trennung von (z.B. kardialen) Nutzsignalen und Störsignalen typisch für die kontaktlosen Messtechniken. Das Potential der BSS kann praktisch nur ausgeschöpft werden, wenn (1) ein geeignetes BSS-Modell verwendet wird, welches der Komplexität der Multikanal-Messung gerecht wird und (2) die unbestimmte Permutation unter den BSS-Ausgangssignalen gelöst wird, d.h. das Nutzsignal praktisch automatisiert identifiziert werden kann. Die vorliegende Arbeit entwirft ein Framework, mit dessen Hilfe die Effizienz von BSS-Algorithmen im Kontext des kamera-basierten Photoplethysmogramms bewertet werden kann. Empfehlungen zur Auswahl bestimmter Algorithmen im Zusammenhang mit spezifischen Signal-Charakteristiken werden abgeleitet. Außerdem werden im Rahmen der Arbeit Konzepte für die automatisierte Kanalauswahl nach BSS im Bereich der kontaktlosen Messung des Elektrokardiogramms entwickelt und bewertet. Neuartige Algorithmen basierend auf Sparse Coding erwiesen sich dabei als besonders effizient im Vergleich zu Standard-Methoden.(Spatio-temporal) Blind Source Separation (BSS) provides a large potential to process distorted multichannel biosignal measurements in the context of novel contact-less recording techniques for separating distortions from the cardiac signal of interest. This potential can only be practically utilized (1) if a BSS model is applied that matches the complexity of the measurement, i.e. the signal mixture and (2) if permutation indeterminacy is solved among the BSS output components, i.e the component of interest can be practically selected. The present work, first, designs a framework to assess the efficacy of BSS algorithms in the context of the camera-based photoplethysmogram (cbPPG) and characterizes multiple BSS algorithms, accordingly. Algorithm selection recommendations for certain mixture characteristics are derived. Second, the present work develops and evaluates concepts to solve permutation indeterminacy for BSS outputs of contact-less electrocardiogram (ECG) recordings. The novel approach based on sparse coding is shown to outperform the existing concepts of higher order moments and frequency-domain features

Physiological monitoring technique using unattached sensors

Electronic instrumentation for body impedance change and electrocardiographic measurements using unattached electrode

A general framework for improving electrocardiography monitoring system with machine learning

As one of the most important health monitoring systems, electrocardiography (ECG) is used to obtain information about the structure and functions of the human heart for detecting and preventing cardiovascular disease. Given its important role, it is vital that the ECG monitoring system provides relevant and accurate information about the heart. Over the years, numerous attempts were made to design and develop more effective ECG monitoring system. Nonetheless, the literature reveals not only several limitations in conventional ECG monitoring system but also emphasizes on the need to adopt new technology such as machine learning to improve the monitoring system as well as its medical applications. This paper reviews previous works on machine learning to explain its key features, capabilities as well as presents a general framework for improving ECG monitoring system

In-bed vital signs monitoring system based on unobtrusive magnetic induction method with a concentric planar gradiometer

Significance. Unobtrusive vital signs monitoring is of major importance for various medical areas such as detection and treatment of sleep disorders, monitoring neonates and burned victims, home health care and smart home applications and wearables among others. Such applications call for monitoring methods in which the patient's natural state is less interfered with. An ideal device would be non-invasive, minimally restrictive, robust enough to compensate movements of the patients, and would operate without relying on the patient's full cooperation. Objective. This paper focuses on the design and development of an unobtrusive vital signs monitoring system particularly suited for long-term monitoring placed under the mattresses. Approach. The system is based on the magnetic induction sensing method, designed to infer presence on the bed, breathing and cardiac activity, and consists of two coils for excitation and detection. The new detection coil is based on a concentric planar gradiometer for canceling the primary field. The signal acquisition system has been designed using simple electronics to avoid ending up with a complex and expensive system. The experimental results were compared with reference signals coming from other known sensors with different technical bases for benchmarking and identifying the advantages and/or drawbacks of the new system regarding other techniques. The designed system was also studied in regards to safety standards and limitations for the exposure to the magnetic fields. Main results. Experimental results confirm the suitability and safety of the sensor for long-term cardiac and respiratory monitoring. The system is able to detect respiration and cardiac activity as well as the presence on the bed and changes in position.Peer ReviewedPreprin

A wearable heart monitor at the ear using ballistocardiogram (BCG) and electrocardiogram (ECG) with a nanowatt ECG heartbeat detection circuit

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (p. 132-137).This work presents a wearable heart monitor at the ear that uses the ballistocardiogram (BCG) and the electrocardiogram (ECG) to extract heart rate, stroke volume, and pre-ejection period (PEP) for the application of continuous heart monitoring. Being a natural anchoring point, the ear is demonstrated as a viable location for the integrated sensing of physiological signals. The source of periodic head movements is identified as a type of BCG, which is measured using an accelerometer. The head BCG's principal peaks (J-waves) are synchronized to heartbeats. Ensemble averaging is used to obtain consistent J-wave amplitudes, which are related to stroke volume. The ECG is sensed locally near the ear using a single-lead configuration. When the BCG and the ECG are used together, an electromechanical duration called the RJ interval can be obtained. Because both head BCG and ECG have low signal-to-noise ratios, cross-correlation is used to statistically extract the RJ interval. The ear-worn device is wirelessly connected to a computer for real time data recording. A clinical test involving hemodynamic maneuvers is performed on 13 subjects. The results demonstrate a linear relationship between the J-wave amplitude and stroke volume, and a linear relationship between the RJ interval and PEP. While the clinical device uses commercial components, a custom integrated circuit for ECG heartbeat detection is designed with the goal of reducing power consumption and device size. With 58nW of power consumption, the ECG circuit replaces the traditional instrumentation amplifier, analog-to-digital converter, and signal processor with a single chip solution. The circuit demonstrates a topology that takes advantage of the ECG's characteristics to extract R-wave timings at the chest and the ear in the presence of baseline drift, muscle artifact, and signal clipping.by David Da He.Ph.D

Sensing with Earables: A Systematic Literature Review and Taxonomy of Phenomena

Earables have emerged as a unique platform for ubiquitous computing by augmenting ear-worn devices with state-of-the-art sensing. This new platform has spurred a wealth of new research exploring what can be detected on a wearable, small form factor. As a sensing platform, the ears are less susceptible to motion artifacts and are located in close proximity to a number of important anatomical structures including the brain, blood vessels, and facial muscles which reveal a wealth of information. They can be easily reached by the hands and the ear canal itself is affected by mouth, face, and head movements. We have conducted a systematic literature review of 271 earable publications from the ACM and IEEE libraries. These were synthesized into an open-ended taxonomy of 47 different phenomena that can be sensed in, on, or around the ear. Through analysis, we identify 13 fundamental phenomena from which all other phenomena can be derived, and discuss the different sensors and sensing principles used to detect them. We comprehensively review the phenomena in four main areas of (i) physiological monitoring and health, (ii) movement and activity, (iii) interaction, and (iv) authentication and identification. This breadth highlights the potential that earables have to offer as a ubiquitous, general-purpose platform