Wearable Wireless Devices

No abstract available

Wearable Wireless Devices

No abstract available

Development of Advanced Closed-Loop Brain Electrophysiology Systems for Freely Behaving Rodents

[ES] La electrofisiología extracelular es una técnica ampliamente usada en investigación neurocientífica, la cual estudia el funcionamiento del cerebro mediante la medición de campos eléctricos generados por la actividad neuronal. Esto se realiza a través de electrodos implantados en el cerebro y conectados a dispositivos electrónicos para amplificación y digitalización de las señales. De los muchos modelos animales usados en experimentación, las ratas y los ratones se encuentran entre las especies más comúnmente utilizadas. Actualmente, la experimentación electrofisiológica busca condiciones cada vez más complejas, limitadas por la tecnología de los dispositivos de adquisición. Dos aspectos son de particular interés: Realimentación de lazo cerrado y comportamiento en condiciones naturales. En esta tesis se presentan desarrollos con el objetivo de mejorar diferentes facetas de estos dos problemas. La realimentación en lazo cerrado se refiere a todas las técnicas en las que los estímulos son producidos en respuesta a un evento generado por el animal. La latencia debe ajustarse a las escalas temporales bajo estudio. Los sistemas modernos de adquisición presentan latencias en el orden de los 10ms. Sin embargo, para responder a eventos rápidos, como pueden ser los potenciales de acción, se requieren latencias por debajo de 1ms. Además, los algoritmos para detectar los eventos o generar los estímulos pueden ser complejos, integrando varias entradas de datos en tiempo real. Integrar el desarrollo de dichos algoritmos en las herramientas de adquisición forma parte del diseño experimental. Para estudiar comportamientos naturales, los animales deben ser capaces de moverse libremente en entornos emulando condiciones naturales. Experimentos de este tipo se ven dificultados por la naturaleza cableada de los sistemas de adquisición. Otras restricciones físicas, como el peso de los implantes o limitaciones en el consumo de energía, pueden también afectar a la duración de los experimentos, limitándola. La experimentación puede verse enriquecida cuando los datos electrofisiológicos se ven complementados con múltiples fuentes distintas. Por ejemplo, seguimiento de los animales o miscroscopía. Herramientas capaces de integrar datos independientemente de su origen abren la puerta a nuevas posibilidades. Los avances tecnológicos presentados abordan estas limitaciones. Se han diseñado dispositivos con latencias de lazo cerrado inferiores a 200us que permiten combinar cientos de canales electrofisiológicos con otras fuentes de datos, como vídeo o seguimiento. El software de control para estos dispositivos se ha diseñado manteniendo la flexibilidad como objetivo. Se han desarrollado interfaces y estándares de naturaleza abierta para incentivar el desarrollo de herramientas compatibles entre ellas. Para resolver los problemas de cableado se siguieron dos métodos distintos. Uno fue el desarrollo de headstages ligeros combinados con cables coaxiales ultra finos y conmutadores activos, gracias al seguimiento de animales. Este desarrollo permite reducir el esfuerzo impuesto a los animales, permitiendo espacios amplios y experimentos de larga duración, al tiempo que permite el uso de headstages con características avanzadas. Paralelamente se desarrolló un tipo diferente de headstage, con tecnología inalámbrica. Se creó un algoritmo de compresión digital especializado capaz de reducir el ancho de banda a menos del 65% de su tamaño original, ahorrando energía. Esta reducción permite baterías más ligeras y mayores tiempos de operación. El algoritmo fue diseñado para ser capaz de ser implementado en una gran variedad de dispositivos. Los desarrollos presentados abren la puerta a nuevas posibilidades experimentales para la neurociencia, combinando adquisición elextrofisiológica con estudios conductuales en condiciones naturales y estímulos complejos en tiempo real.[CA] L'electrofisiologia extracel·lular és una tècnica àmpliament utilitzada en la investigació neurocientífica, la qual permet estudiar el funcionament del cervell mitjançant el mesurament de camps elèctrics generats per l'activitat neuronal. Això es realitza a través d'elèctrodes implantats al cervell, connectats a dispositius electrònics per a l'amplificació i digitalització dels senyals. Dels molts models animals utilitzats en experimentació electrofisiològica, les rates i els ratolins es troben entre les espècies més utilitzades. Actualment, l'experimentació electrofisiològica busca condicions cada vegada més complexes, limitades per la tecnologia dels dispositius d'adquisició. Dos aspectes són d'especial interès: La realimentació de sistemes de llaç tancat i el comportament en condicions naturals. En aquesta tesi es presenten desenvolupaments amb l'objectiu de millorar diferents aspectes d'aquestos dos problemes. La realimentació de sistemes de llaç tancat es refereix a totes aquestes tècniques on els estímuls es produeixen en resposta a un esdeveniment generat per l'animal. La latència ha d'ajustar-se a les escales temporals sota estudi. Els sistemes moderns d'adquisició presenten latències en l'ordre dels 10ms. No obstant això, per a respondre a esdeveniments ràpids, com poden ser els potencials d'acció, es requereixen latències per davall de 1ms. A més a més, els algoritmes per a detectar els esdeveniments o generar els estímuls poden ser complexos, integrant varies entrades de dades a temps real. Integrar el desenvolupament d'aquests algoritmes en les eines d'adquisició forma part del disseny dels experiments. Per a estudiar comportaments naturals, els animals han de ser capaços de moure's lliurement en ambients emulant condicions naturals. Aquestos experiments es veuen limitats per la natura cablejada dels sistemes d'adquisició. Altres restriccions físiques, com el pes dels implants o el consum d'energia, poden també limitar la duració dels experiments. L'experimentació es pot enriquir quan les dades electrofisiològiques es complementen amb dades de múltiples fonts. Per exemple, el seguiment d'animals o microscòpia. Eines capaces d'integrar dades independentment del seu origen obrin la porta a noves possibilitats. Els avanços tecnològics presentats tracten aquestes limitacions. S'han dissenyat dispositius amb latències de llaç tancat inferiors a 200us que permeten combinar centenars de canals electrofisiològics amb altres fonts de dades, com vídeo o seguiment. El software de control per a aquests dispositius s'ha dissenyat mantenint la flexibilitat com a objectiu. S'han desenvolupat interfícies i estàndards de naturalesa oberta per a incentivar el desenvolupament d'eines compatibles entre elles. Per a resoldre els problemes de cablejat es van seguir dos mètodes diferents. Un va ser el desenvolupament de headstages lleugers combinats amb cables coaxials ultra fins i commutadors actius, gràcies al seguiment d'animals. Aquest desenvolupament permet reduir al mínim l'esforç imposat als animals, permetent espais amplis i experiments de llarga durada, al mateix temps que permet l'ús de headstages amb característiques avançades. Paral·lelament es va desenvolupar un tipus diferent de headstage, amb tecnologia sense fil. Es va crear un algorisme de compressió digital especialitzat capaç de reduir l'amplada de banda a menys del 65% de la seua grandària original, estalviant energia. Aquesta reducció permet bateries més lleugeres i majors temps d'operació. L'algorisme va ser dissenyat per a ser capaç de ser implementat a una gran varietat de dispositius. Els desenvolupaments presentats obrin la porta a noves possibilitats experimentals per a la neurociència, combinant l'adquisició electrofisiològica amb estudis conductuals en condicions naturals i estímuls complexos en temps real.[EN] Extracellular electrophysiology is a technique widely used in neuroscience research. It can offer insights on how the brain works by measuring the electrical fields generated by neural activity. This is done through electrodes implanted in the brain and connected to amplification and digitization electronic circuitry. Of the many animal models used in electrophysiology experimentation, rodents such as rats and mice are among the most popular species. Modern electrophysiology experiments seek increasingly complex conditions that are limited by acquisition hardware technology. Two particular aspects are of special interest: Closed-loop feedback and naturalistic behavior. In this thesis, we present developments aiming to improve on different facets of these two problems. Closed-loop feedback encompasses all techniques in which stimuli is produced in response of an event generated by the animal. Latency, the time between trigger event and stimuli generation, must adjust to the biological timescale being studied. While modern acquisition systems feature latencies in the order of 10ms, response to fast events such as high-frequency electrical transients created by neuronal activity require latencies under $1ms$. In addition, algorithms for triggering or generating closed-loop stimuli can be complex, integrating multiple inputs in real-time. Integration of algorithm development into acquisition tools becomes an important part of experiment design. For electrophysiology experiments featuring naturalistic behavior, animals must be able to move freely in ecologically meaningful environments, mimicking natural conditions. Experiments featuring elements such as large arenaa, environmental objects or the presence of another animals are, however, hindered by the wired nature of acquisition systems. Other physical constraints, such as implant weight or power restrictions can also affect experiment time, limiting their duration. Beyond the technical limits, complex experiments are enriched when electrophysiology data is integrated with multiple sources, for example animal tracking or brain microscopy. Tools allowing mixing data independently of the source open new experimental possibilities. The technological advances presented on this thesis addresses these topics. We have designed devices with closed-loop latencies under 200us while featuring high-bandwidth interfaces. These allow the simultaneous acquisition of hundreds of electrophysiological channels combined with other heterogeneous data sources, such as video or tracking. The control software for these devices was designed with flexibility in mind, allowing easy implementation of closed-loop algorithms. Open interface standards were created to encourage the development of interoperable tools for experimental data integration. To solve wiring issues in behavioral experiments, we followed two different approaches. One was the design of light headstages, coupled with ultra-thin coaxial cables and active commutator technology, making use of animal tracking. This allowed to reduce animal strain to a minimum allowing large arenas and prolonged experiments with advanced headstages. A different, wireless headstage was also developed. We created a digital compression algorithm specialized for neural electrophysiological signals able to reduce data bandwidth to less than 65.5% its original size without introducing distortions. Bandwidth has a large effect on power requirements. Thus, this reduction allows for lighter batteries and extended operational time. The algorithm is designed to be able to be implemented in a wide variety of devices, requiring low hardware resources and adding negligible power requirements to a system. Combined, the developments we present open new possibilities for neuroscience experiments combining electrophysiology acquisition with natural behaviors and complex, real-time, stimuli.The research described in this thesis was carried out at the Polytechnic University of Valencia (Universitat Politècnica de València), Valencia, Spain in an extremely close collaboration with the Neuroscience Institute - Spanish National Research Council - Miguel Hernández University (Instituto de Neurociencias - Consejo Superior de Investigaciones Cientí cas - Universidad Miguel Hernández), San Juan de Alicante, Spain. The projects described in chapters 3 and 4 were developed in collabo- ration with, and funded by, Open Ephys, Cambridge, MA, USA and OEPS - Eléctronica e produção, unipessoal lda, Algés, Portugal.Cuevas López, A. (2021). Development of Advanced Closed-Loop Brain Electrophysiology Systems for Freely Behaving Rodents [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/179718TESI

Recent Developments in Smart Healthcare

Medicine is undergoing a sector-wide transformation thanks to the advances in computing and networking technologies. Healthcare is changing from reactive and hospital-centered to preventive and personalized, from disease focused to well-being centered. In essence, the healthcare systems, as well as fundamental medicine research, are becoming smarter. We anticipate significant improvements in areas ranging from molecular genomics and proteomics to decision support for healthcare professionals through big data analytics, to support behavior changes through technology-enabled self-management, and social and motivational support. Furthermore, with smart technologies, healthcare delivery could also be made more efficient, higher quality, and lower cost. In this special issue, we received a total 45 submissions and accepted 19 outstanding papers that roughly span across several interesting topics on smart healthcare, including public health, health information technology (Health IT), and smart medicine

Telemedicine

Telemedicine is a rapidly evolving field as new technologies are implemented for example for the development of wireless sensors, quality data transmission. Using the Internet applications such as counseling, clinical consultation support and home care monitoring and management are more and more realized, which improves access to high level medical care in underserved areas. The 23 chapters of this book present manifold examples of telemedicine treating both theoretical and practical foundations and application scenarios

Distant Operational Care Centre: Design Project Report

The goal of this project is to outline the design of the Distant Operational Care Centre (DOCC), a modular medical facility to maintain human health and performance in space, that is adaptable to a range of remote human habitats. The purpose of this project is to outline a design, not to go into a complete technical specification of a medical facility for space. This project involves a process to produce a concise set of requirements, addressing the fundamental problems and issues regarding all aspects of a space medical facility for the future. The ideas presented here are at a high level, based on existing, researched, and hypothetical technologies. Given the long development times for space exploration, the outlined concepts from this project embodies a collection of identified problems, and corresponding proposed solutions and ideas, ready to contribute to future space exploration efforts. In order to provide a solid extrapolation and speculation in the context of the future of space medicine, the extent of this project's vision is roughly within the next two decades. The Distant Operational Care Centre (DOCC) is a modular medical facility for space. That is, its function is to maintain human health and performance in space environments, through prevention, diagnosis, and treatment. Furthermore, the DOCC must be adaptable to meet the environmental requirements of different remote human habitats, and support a high quality of human performance. To meet a diverse range of remote human habitats, the DOCC concentrates on a core medical capability that can then be adapted. Adaptation would make use of the DOCC's functional modularity, providing the ability to replace, add, and modify core functions of the DOCC by updating hardware, operations, and procedures. Some of the challenges to be addressed by this project include what constitutes the core medical capability in terms of hardware, operations, and procedures, and how DOCC can be adapted to different remote habitats

Digital CMOS ISFET architectures and algorithmic methods for point-of-care diagnostics

Over the past decade, the surge of infectious diseases outbreaks across the globe is redefining how healthcare is provided and delivered to patients, with a clear trend towards distributed diagnosis at the Point-of-Care (PoC). In this context, Ion-Sensitive Field Effect Transistors (ISFETs) fabricated on standard CMOS technology have emerged as a promising solution to achieve a precise, deliverable and inexpensive platform that could be deployed worldwide to provide a rapid diagnosis of infectious diseases. This thesis presents advancements for the future of ISFET-based PoC diagnostic platforms, proposing and implementing a set of hardware and software methodologies to overcome its main challenges and enhance its sensing capabilities. The first part of this thesis focuses on novel hardware architectures that enable direct integration with computational capabilities while providing pixel programmability and adaptability required to overcome pressing challenges on ISFET-based PoC platforms. This section explores oscillator-based ISFET architectures, a set of sensing front-ends that encodes the chemical information on the duty cycle of a PWM signal. Two initial architectures are proposed and fabricated in AMS 0.35um, confirming multiple degrees of programmability and potential for multi-sensing. One of these architectures is optimised to create a dual-sensing pixel capable of sensing both temperature and chemical information on the same spatial point while modulating this information simultaneously on a single waveform. This dual-sensing capability, verified in silico using TSMC 0.18um process, is vital for DNA-based diagnosis where protocols such as LAMP or PCR require precise thermal control. The COVID-19 pandemic highlighted the need for a deliverable diagnosis that perform nucleic acid amplification tests at the PoC, requiring minimal footprint by integrating sensing and computational capabilities. In response to this challenge, a paradigm shift is proposed, advocating for integrating all elements of the portable diagnostic platform under a single piece of silicon, realising a ``Diagnosis-on-a-Chip". This approach is enabled by a novel Digital ISFET Pixel that integrates both ADC and memory with sensing elements on each pixel, enhancing its parallelism. Furthermore, this architecture removes the need for external instrumentation or memories and facilitates its integration with computational capabilities on-chip, such as the proposed ARM Cortex M3 system. These computational capabilities need to be complemented with software methods that enable sensing enhancement and new applications using ISFET arrays. The second part of this thesis is devoted to these methods. Leveraging the programmability capabilities available on oscillator-based architectures, various digital signal processing algorithms are implemented to overcome the most urgent ISFET non-idealities, such as trapped charge, drift and chemical noise. These methods enable fast trapped charge cancellation and enhanced dynamic range through real-time drift compensation, achieving over 36 hours of continuous monitoring without pixel saturation. Furthermore, the recent development of data-driven models and software methods open a wide range of opportunities for ISFET sensing and beyond. In the last section of this thesis, two examples of these opportunities are explored: the optimisation of image compression algorithms on chemical images generated by an ultra-high frame-rate ISFET array; and a proposed paradigm shift on surface Electromyography (sEMG) signals, moving from data-harvesting to information-focused sensing. These examples represent an initial step forward on a journey towards a new generation of miniaturised, precise and efficient sensors for PoC diagnostics.Open Acces

Ultra low power wearable sleep diagnostic systems

Sleep disorders are studied using sleep study systems called Polysomnography that records several biophysical parameters during sleep. However, these are bulky and are typically located in a medical facility where patient monitoring is costly and quite inefficient. Home-based portable systems solve these problems to an extent but they record only a minimal number of channels due to limited battery life. To surmount this, wearable sleep system are desired which need to be unobtrusive and have long battery life. In this thesis, a novel sleep system architecture is presented that enables the design of an ultra low power sleep diagnostic system. This architecture is capable of extending the recording time to 120 hours in a wearable system which is an order of magnitude improvement over commercial wearable systems that record for about 12 hours. This architecture has in effect reduced the average power consumption of 5-6 mW per channel to less than 500 uW per channel. This has been achieved by eliminating sampled data architecture, reducing the wireless transmission rate and by moving the sleep scoring to the sensors. Further, ultra low power instrumentation amplifiers have been designed to operate in weak inversion region to support this architecture. A 40 dB chopper-stabilised low power instrumentation amplifiers to process EEG were designed and tested to operate from 1.0 V consuming just 3.1 uW for peak mode operation with DC servo loop. A 50 dB non-EEG amplifier continuous-time bandpass amplifier with a consumption of 400 nW was also fabricated and tested. Both the amplifiers achieved a high CMRR and impedance that are critical for wearable systems. Combining these amplifiers with the novel architecture enables the design of an ultra low power sleep recording system. This reduces the size of the battery required and hence enables a truly wearable system.Open Acces

CMOS Hyperbolic Sine ELIN filters for low/audio frequency biomedical applications

Hyperbolic-Sine (Sinh) filters form a subclass of Externally-Linear-Internally-Non- Linear (ELIN) systems. They can handle large-signals in a low power environment under half the capacitor area required by the more popular ELIN Log-domain filters. Their inherent class-AB nature stems from the odd property of the sinh function at the heart of their companding operation. Despite this early realisation, the Sinh filtering paradigm has not attracted the interest it deserves to date probably due to its mathematical and circuit-level complexity. This Thesis presents an overview of the CMOS weak inversion Sinh filtering paradigm and explains how biomedical systems of low- to audio-frequency range could benefit from it. Its dual scope is to: consolidate the theory behind the synthesis and design of high order Sinh continuous–time filters and more importantly to confirm their micro-power consumption and 100+ dB of DR through measured results presented for the first time. Novel high order Sinh topologies are designed by means of a systematic mathematical framework introduced. They employ a recently proposed CMOS Sinh integrator comprising only p-type devices in its translinear loops. The performance of the high order topologies is evaluated both solely and in comparison with their Log domain counterparts. A 5th order Sinh Chebyshev low pass filter is compared head-to-head with a corresponding and also novel Log domain class-AB topology, confirming that Sinh filters constitute a solution of equally high DR (100+ dB) with half the capacitor area at the expense of higher complexity and power consumption. The theoretical findings are validated by means of measured results from an 8th order notch filter for 50/60Hz noise fabricated in a 0.35μm CMOS technology. Measured results confirm a DR of 102dB, a moderate SNR of ~60dB and 74μW power consumption from 2V power supply

Intelligent Sensor Networks

In the last decade, wireless or wired sensor networks have attracted much attention. However, most designs target general sensor network issues including protocol stack (routing, MAC, etc.) and security issues. This book focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on their world-class research, the authors present the fundamentals of intelligent sensor networks. They cover sensing and sampling, distributed signal processing, and intelligent signal learning. In addition, they present cutting-edge research results from leading experts