Active Contact Lens - Low Power Backscattering Sensor-to-Antenna Integration Circuitry

The Active Contact Lens measures the cornea-scleral radius of the wearer’s eye, which correlates to intraocular pressure (IOP), Glaucoma’s primary indicator. IOP varies throughout the day, and is drastically different from person to person, so constantly measuring it over a period of a few days can provide individualized tracking of the disease’s development and will help doctors develop personalized treatment schedules to treat the disease more precisely. The Active Contact Lens sensor measures strain, based on the cornea-scleral radius, and reports the results wirelessly, to allow monitoring of an individual’s IOP over time. The lens is powered similarly to a passive RFID tag, so the device can operate long-term. The contact lens itself is clear, and biologically safe for the user. This allows the user to wear the device when asked by their doctor with no medical repercussions. This document proposes part of the sensor-to-antenna integration circuitry. The whole design consists of three stages. The first stage is a Wheatstone bridge which converts the sensor’s varying resistance into an analog voltage. The second stage is a biasing circuit which receives the analog voltage, amplifies it, and sets it within the determined input range for the third stage. The third and final stage, which this document focuses on, is a current-starved voltage-controlled ring oscillator (CSVCRO) that translates the voltage to frequency. The oscillator consists of 21 current starved CMOS inverters controlled by a current mirror biasing circuit. The final design has a frequency range of 736Hz to 38.58MHz, and an average power dissipation of 784.7 at center frequency 23.4MHz. All circuitry was designed and simulated using 180nm CMOS technology

Development of a wireless intra-ocular pressure monitoring system for incorporation into a therapeutic glaucoma drainage implant

http://spiedigitallibrary.org/journals/doc/SPIEDL-home/proc

FM Continuous Monitoring of Intraocular Pressure, an Engineering Perspective

This chapter discusses the problem of continuously monitoring intraocular pressure (IOP) from an engineering perspective. It is aimed to all public in general although we think that medical staff and engineers may benefit the most from it. Although equations are included for engineers to get a glimpse of how the system works, this chapter does not go into great detail in mathematics and physics to make it understandable to medical staff. It provides though references for engineers who wish to get a better understanding of key subjects tackled in this chapter. The chapter is organized as follows: Section 1 introduces intraocular pressure (IOP) and need for its continuous monitoring. Section 2 describes the most recent efforts to develop a continuous IOP monitoring system. Section 3 shows what medical and engineering considerations must be taken into account to effectively measure IOP. Section 4 deals with health issues due to tissue warming and how to prevent them. Section 5 explains how an implant can be fabricated using either passive electronic components or active ones. Finally, Section 6 explains how the pressure sensor and the electronic circuits can be integrated

On the use of smartphones as novel photogrammetric water gauging instruments: Developing tools for crowdsourcing water levels

The term global climate change is omnipresent since the beginning of the last decade. Changes in the global climate are associated with an increase in heavy rainfalls that can cause nearly unpredictable flash floods. Consequently, spatio-temporally high-resolution monitoring of rivers becomes increasingly important. Water gauging stations continuously and precisely measure water levels. However, they are rather expensive in purchase and maintenance and are preferably installed at water bodies relevant for water management. Small-scale catchments remain often ungauged. In order to increase the data density of hydrometric monitoring networks and thus to improve the prediction quality of flood events, new, flexible and cost-effective water level measurement technologies are required. They should be oriented towards the accuracy requirements of conventional measurement systems and facilitate the observation of water levels at virtually any time, even at the smallest rivers. A possible solution is the development of a photogrammetric smartphone application (app) for crowdsourcing water levels, which merely requires voluntary users to take pictures of a river section to determine the water level. Today’s smartphones integrate high-resolution cameras, a variety of sensors, powerful processors, and mass storage. However, they are designed for the mass market and use low-cost hardware that cannot comply with the quality of geodetic measurement technology. In order to investigate the potential for mobile measurement applications, research was conducted on the smartphone as a photogrammetric measurement instrument as part of the doctoral project. The studies deal with the geometric stability of smartphone cameras regarding device-internal temperature changes and with the accuracy potential of rotation parameters measured with smartphone sensors. The results show a high, temperature-related variability of the interior orientation parameters, which is why the calibration of the camera should be carried out during the immediate measurement. The results of the sensor investigations show considerable inaccuracies when measuring rotation parameters, especially the compass angle (errors up to 90° were observed). The same applies to position parameters measured by global navigation satellite system (GNSS) receivers built into smartphones. According to the literature, positional accuracies of about 5 m are possible in best conditions. Otherwise, errors of several 10 m are to be expected. As a result, direct georeferencing of image measurements using current smartphone technology should be discouraged. In consideration of the results, the water gauging app Open Water Levels (OWL) was developed, whose methodological development and implementation constituted the core of the thesis project. OWL enables the flexible measurement of water levels via crowdsourcing without requiring additional equipment or being limited to specific river sections. Data acquisition and processing take place directly in the field, so that the water level information is immediately available. In practice, the user captures a short time-lapse sequence of a river bank with OWL, which is used to calculate a spatio-temporal texture that enables the detection of the water line. In order to translate the image measurement into 3D object space, a synthetic, photo-realistic image of the situation is created from existing 3D data of the river section to be investigated. Necessary approximations of the image orientation parameters are measured by smartphone sensors and GNSS. The assignment of camera image and synthetic image allows for the determination of the interior and exterior orientation parameters by means of space resection and finally the transfer of the image-measured 2D water line into the 3D object space to derive the prevalent water level in the reference system of the 3D data. In comparison with conventionally measured water levels, OWL reveals an accuracy potential of 2 cm on average, provided that synthetic image and camera image exhibit consistent image contents and that the water line can be reliably detected. In the present dissertation, related geometric and radiometric problems are comprehensively discussed. Furthermore, possible solutions, based on advancing developments in smartphone technology and image processing as well as the increasing availability of 3D reference data, are presented in the synthesis of the work. The app Open Water Levels, which is currently available as a beta version and has been tested on selected devices, provides a basis, which, with continuous further development, aims to achieve a final release for crowdsourcing water levels towards the establishment of new and the expansion of existing monitoring networks.Der Begriff des globalen Klimawandels ist seit Beginn des letzten Jahrzehnts allgegenwärtig. Die Veränderung des Weltklimas ist mit einer Zunahme von Starkregenereignissen verbunden, die nahezu unvorhersehbare Sturzfluten verursachen können. Folglich gewinnt die raumzeitlich hochaufgelöste Überwachung von Fließgewässern zunehmend an Bedeutung. Pegelmessstationen erfassen kontinuierlich und präzise Wasserstände, sind jedoch in Anschaffung und Wartung sehr teuer und werden vorzugsweise an wasserwirtschaftlich-relevanten Gewässern installiert. Kleinere Gewässer bleiben häufig unbeobachtet. Um die Datendichte hydrometrischer Messnetze zu erhöhen und somit die Vorhersagequalität von Hochwasserereignissen zu verbessern, sind neue, kostengünstige und flexibel einsetzbare Wasserstandsmesstechnologien erforderlich. Diese sollten sich an den Genauigkeitsanforderungen konventioneller Messsysteme orientieren und die Beobachtung von Wasserständen zu praktisch jedem Zeitpunkt, selbst an den kleinsten Flüssen, ermöglichen. Ein Lösungsvorschlag ist die Entwicklung einer photogrammetrischen Smartphone-Anwendung (App) zum Crowdsourcing von Wasserständen mit welcher freiwillige Nutzer lediglich Bilder eines Flussabschnitts aufnehmen müssen, um daraus den Wasserstand zu bestimmen. Heutige Smartphones integrieren hochauflösende Kameras, eine Vielzahl von Sensoren, leistungsfähige Prozessoren und Massenspeicher. Sie sind jedoch für den Massenmarkt konzipiert und verwenden kostengünstige Hardware, die nicht der Qualität geodätischer Messtechnik entsprechen kann. Um das Einsatzpotential in mobilen Messanwendungen zu eruieren, sind Untersuchungen zum Smartphone als photogrammetrisches Messinstrument im Rahmen des Promotionsprojekts durchgeführt worden. Die Studien befassen sich mit der geometrischen Stabilität von Smartphone-Kameras bezüglich geräteinterner Temperaturänderungen und mit dem Genauigkeitspotential von mit Smartphone-Sensoren gemessenen Rotationsparametern. Die Ergebnisse zeigen eine starke, temperaturbedingte Variabilität der inneren Orientierungsparameter, weshalb die Kalibrierung der Kamera zum unmittelbaren Messzeitpunkt erfolgen sollte. Die Ergebnisse der Sensoruntersuchungen zeigen große Ungenauigkeiten bei der Messung der Rotationsparameter, insbesondere des Kompasswinkels (Fehler von bis zu 90° festgestellt). Selbiges gilt auch für Positionsparameter, gemessen durch in Smartphones eingebaute Empfänger für Signale globaler Navigationssatellitensysteme (GNSS). Wie aus der Literatur zu entnehmen ist, lassen sich unter besten Bedingungen Lagegenauigkeiten von etwa 5 m erreichen. Abseits davon sind Fehler von mehreren 10 m zu erwarten. Infolgedessen ist von einer direkten Georeferenzierung von Bildmessungen mittels aktueller Smartphone-Technologie abzusehen. Unter Berücksichtigung der gewonnenen Erkenntnisse wurde die Pegel-App Open Water Levels (OWL) entwickelt, deren methodische Entwicklung und Implementierung den Kern der Arbeit bildete. OWL ermöglicht die flexible Messung von Wasserständen via Crowdsourcing, ohne dabei zusätzliche Ausrüstung zu verlangen oder auf spezifische Flussabschnitte beschränkt zu sein. Datenaufnahme und Verarbeitung erfolgen direkt im Feld, so dass die Pegelinformationen sofort verfügbar sind. Praktisch nimmt der Anwender mit OWL eine kurze Zeitraffersequenz eines Flussufers auf, die zur Berechnung einer Raum-Zeit-Textur dient und die Erkennung der Wasserlinie ermöglicht. Zur Übersetzung der Bildmessung in den 3D-Objektraum wird aus vorhandenen 3D-Daten des zu untersuchenden Flussabschnittes ein synthetisches, photorealistisches Abbild der Aufnahmesituation erstellt. Erforderliche Näherungen der Bildorientierungsparameter werden von Smartphone-Sensoren und GNSS gemessen. Die Zuordnung von Kamerabild und synthetischem Bild erlaubt die Bestimmung der inneren und äußeren Orientierungsparameter mittels räumlichen Rückwärtsschnitt. Nach Rekonstruktion der Aufnahmesituation lässt sich die im Bild gemessene 2D-Wasserlinie in den 3D-Objektraum projizieren und der vorherrschende Wasserstand im Referenzsystem der 3D-Daten ableiten. Im Soll-Ist-Vergleich mit konventionell gemessenen Pegeldaten zeigt OWL ein erreichbares Genauigkeitspotential von durchschnittlich 2 cm, insofern synthetisches und reales Kamerabild einen möglichst konsistenten Bildinhalt aufweisen und die Wasserlinie zuverlässig detektiert werden kann. In der vorliegenden Dissertation werden damit verbundene geometrische und radiometrische Probleme ausführlich diskutiert sowie Lösungsansätze, auf der Basis fortschreitender Entwicklungen von Smartphone-Technologie und Bildverarbeitung sowie der zunehmenden Verfügbarkeit von 3D-Referenzdaten, in der Synthese der Arbeit vorgestellt. Mit der gegenwärtig als Betaversion vorliegenden und auf ausgewählten Geräten getesteten App Open Water Levels wurde eine Basis geschaffen, die mit kontinuierlicher Weiterentwicklung eine finale Freigabe für das Crowdsourcing von Wasserständen und damit den Aufbau neuer und die Erweiterung bestehender Monitoring-Netzwerke anstrebt

Updates of Wearing Devices (WDs) In Healthcare, And Disease Monitoring

 With the rising pervasiveness of growing populace, aging and chronic illnesses consistently rising medical services costs, the health care system is going through a crucial change from the conventional hospital focused system to an individual-focused system. Since the twentieth century, wearable sensors are becoming widespread in medical care and biomedical monitoring systems, engaging consistent estimation of biomarkers for checking of the diseased condition and wellbeing, clinical diagnostics and assessment in biological fluids like saliva, blood, and sweat. Recently, the improvements have been centered around electrochemical and optical biosensors, alongside advances with the non-invasive monitoring of biomarkers, bacteria and hormones, etc. Wearable devices have created with a mix of multiplexed biosensing, microfluidic testing and transport frameworks incorporated with flexible materials and body connections for additional created wear ability and effortlessness. These wearables hold guarantee and are fit for a higher understanding of the relationships between analyte focuses inside the blood or non-invasive biofluids and feedback to the patient, which is fundamentally significant in ideal finding, therapy, and control of diseases. In any case, cohort validation studies and execution assessment of wearable biosensors are expected to support their clinical acceptance. In the current review, we discussed the significance, highlights, types of wearables, difficulties and utilizations of wearable devices for biological fluids for the prevention of diseased conditions and real time monitoring of human wellbeing. In this, we sum up the different wearable devices that are developed for health care monitoring and their future potential has been discussed in detail

Automated Vision-Based High Intraocular Pressure Detection Using Frontal Eye Images

Glaucoma, the silent thief of vision, is mostly caused by the gradual increase of pressure in the eye which is known as intraocular pressure (IOP). An effective way to prevent the rise in eye pressure is by early detection. Prior computer vision-based work regarding IOP relies on fundus images of the optic nerves. This paper provides a novel vision-based framework to help in the initial IOP screening using only frontal eye images. The framework first introduces the utilization of a fully convolutional neural (FCN) network on frontal eye images for sclera and iris segmentation. Using these extracted areas, six features that include mean redness level of the sclera, red area percentage, Pupil/Iris diameter ratio, and three sclera contour features (distance, area, and angle) are computed. A database of images from the Princess Basma Hospital is used in this work, containing 400 facial images; 200 cases with normal IOP; and 200 cases with high IOP. Once the features are extracted, two classifiers (support vector machine and decision tree) are applied to obtain the status of the patients in terms of IOP (normal or high). The overall accuracy of the proposed framework is over 97.75% using the decision tree. The novelties and contributions of this work include introducing a fully convolutional network architecture for eye sclera segmentation, in addition to scientifically correlating the frontal eye view (image) with IOP by introducing new sclera contour features that have not been previously introduced in the literature from frontal eye images for IOP status determination.https://doi.org/10.1109/JTEHM.2019.291553

Recent Advances on Implantable Wireless Sensor Networks

Implantable electronic devices are undergoing a miniaturization age, becoming more efficient and yet more powerful as well. Biomedical sensors are used to monitor a multitude of physiological parameters, such as glucose levels, blood pressure and neural activity. A group of sensors working together in the human body is the main component of a body area network, which is a wireless sensor network applied to the human body. In this chapter, applications of wireless biomedical sensors are presented, along with state-of-the-art communication and powering mechanisms of these devices. Furthermore, recent integration methods that allow the sensors to become smaller and more suitable for implantation are summarized. For individual sensors to become a body area network (BAN), they must form a network and work together. Issues that must be addressed when developing these networks are detailed and, finally, mobility methods for implanted sensors are presented

Employing Information and Communications Technologies in Homes and Cities for the Health and Well-Being of Older People

YesHe X and Sheriff RE (Eds.) Employing ICT in Homes and Cities for the Health and Well-Being of Older People. Workshop Proceedings of ICT4HOP’16. 15-17 Aug 2016. Sichuan University, Chengdu, China.British Council, Researcher Links, Newton Fund, NSF

Next generation RFID telemetry design for biomedical implants.

The design and development of a Radio Frequency Identification (RFID) based pressure-sensing system to increase the range of current Intra-Ocular Pressure (IOP) sensing systems is described in this dissertation. A large number of current systems use near-field inductive coupling for the transfer of energy and data, which limits the operational range to only a few centimeters and does not allow for continuous monitoring of pressure. Increasing the powering range of the telemetry system will offer the possibility of continuous monitoring since the reader can be attached to a waist belt or put on a night stand when sleeping. The system developed as part of this research operates at Ultra-High Frequencies (UHF) and makes use of the electromagnetic far field to transfer energy and data, which increases the potential range of operation and allows for the use of smaller antennas. The system uses a novel electrically small antenna (ESA) to receive the incident RF signal. A four stage Schottky circuit rectifies and multiplies the received RF signal and provides DC power to a Colpitts oscillator. The oscillator is connected to a pressure sensor and provides an output signal frequency that is proportional to the change in pressure. The system was fabricated using a mature, inexpensive process. The performance of the system compares well with current state of the art, but uses a smaller antenna and a less expensive fabrication process. The system was able to operate over the desired range of 1 m using a half-wave dipole antenna. It was possible to power the system over a range of at least 6.4 cm when the electrically small antenna was used as the receiving antenna

Wearable devices and IoT applications for symptom detection, infection tracking, and diffusion containment of the COVID-19 pandemic: a survey

Until a safe and effective vaccine to fight the SARS-CoV-2 virus is developed and available for the global population, preventive measures, such as wearable tracking and monitoring systems supported by Internet of Things (IoT) infrastructures, are valuable tools for containing the pandemic. In this review paper we analyze innovative wearable systems for limiting the virus spread, early detection of the first symptoms of the coronavirus disease COVID-19 infection, and remote monitoring of the health conditions of infected patients during the quarantine. The attention is focused on systems allowing quick user screening through ready-to-use hardware and software components. Such sensor-based systems monitor the principal vital signs, detect symptoms related to COVID-19 early, and alert patients and medical staff. Novel wearable devices for complying with social distancing rules and limiting interpersonal contagion (such as smart masks) are investigated and analyzed. In addition, an overview of implantable devices for monitoring the effects of COVID-19 on the cardiovascular system is presented. Then we report an overview of tracing strategies and technologies for containing the COVID-19 pandemic based on IoT technologies, wearable devices, and cloud computing. In detail, we demonstrate the potential of radio frequency based signal technology, including Bluetooth Low Energy (BLE), Wi-Fi, and radio frequency identification (RFID), often combined with Apps and cloud technology. Finally, critical analysis and comparisons of the different discussed solutions are presented, highlighting their potential and providing new insights for developing innovative tools for facing future pandemics