Brain-inspired nanophotonic spike computing:challenges and prospects

Nanophotonic spiking neural networks (SNNs) based on neuron-like excitable subwavelength (submicrometre) devices are of key importance for realizing brain-inspired, power-efficient artificial intelligence (AI) systems with high degree of parallelism and energy efficiency. Despite significant advances in neuromorphic photonics, compact and efficient nanophotonic elements for spiking signal emission and detection, as required for spike-based computation, remain largely unexplored. In this invited perspective, we outline the main challenges, early achievements, and opportunities toward a key-enabling photonic neuro-architecture using III-V/Si integrated spiking nodes based on nanoscale resonant tunnelling diodes (nanoRTDs) with folded negative differential resistance. We utilize nanoRTDs as nonlinear artificial neurons capable of spiking at high-speeds. We discuss the prospects for monolithic integration of nanoRTDs with nanoscale light-emitting diodes and nanolaser diodes, and nanophotodetectors to realize neuron emitter and receiver spiking nodes, respectively. Such layout would have a small footprint, fast operation, and low power consumption, all key requirements for efficient nano-optoelectronic spiking operation. We discuss how silicon photonics interconnects, integrated photorefractive interconnects, and 3D waveguide polymeric interconnections can be used for interconnecting the emitter-receiver spiking photonic neural nodes. Finally, using numerical simulations of artificial neuron models, we present spike-based spatio-temporal learning methods for applications in relevant AI-based functional tasks, such as image pattern recognition, edge detection, and SNNs for inference and learning. Future developments in neuromorphic spiking photonic nanocircuits, as outlined here, will significantly boost the processing and transmission capabilities of next-generation nanophotonic spike-based neuromorphic architectures for energy-efficient AI applications. This perspective paper is a result of the European Union funded research project ChipAI in the frame of the Horizon 2020 Future and Emerging Technologies Open programme.</p

SRAM-Based FPGA Systems for Safety-Critical Applications: A Survey on Design Standards and Proposed Methodologies

As the ASIC design cost becomes affordable only for very large-scale productions, the FPGA technology is currently becoming the leading technology for those applications that require a small-scale production. FPGAs can be considered as a technology crossing between hardware and software. Only a small-number of standards for the design of safety-critical systems give guidelines and recommendations that take the peculiarities of the FPGA technology into consideration. The main contribution of this paper is an overview of the existing design standards that regulate the design and verification of FPGA-based systems in safety-critical application fields. Moreover, the paper proposes a survey of significant published research proposals and existing industrial guidelines about the topic, and collects and reports about some lessons learned from industrial and research projects involving the use of FPGA devices

The physiological effects of blood flow restricted muscle stimulation

Emerging evidence suggests that low load exercise stimuli can lead to significant muscular adaptations when blood flow to a muscle is restricted or occluded. Electrical muscle stimulation has been used for years in the rehabilitative settings, but muscular hypertrophic or oxidative adaptation resulting from electrical stimulation is typically of limited magnitude, likely owing to the discomfort caused by the high intensity stimulus necessary to cause greater adaptation. Combining low intensity transcutaneous electrical muscle stimulation (TEMS) with blood flow restriction (BFR) has yet to be examined, and offers the potential to stimulate substantial muscular adaptation without undue discomfort. We examined the effects of combining BFR with a low intensity TEMS on the upper and lower body musculature. Twenty recreationally active subjects (24±6 yr, 174±10 cm, 70±17kg) were recruited and had every limb randomly allocated to one of four possible training groups: 1) BFR -4mins inflated, 4 mins deflated at 220mmHg alone, 2) TEMS alone- at a maximally tolerable intensity, 3) BFR+TEMS (COMB), or 4) control (CON). Each arm and leg was “trained” in its respective intervention group four times weekly for six weeks. To test muscular adaptation, muscle size and strength were measured at baseline and following 6 weeks of stimuli. Mean differences in size (g) and strength (kg), between week 0 and week 6, were calculated for each intervention group. ARM: After 6 weeks of training, the COMB group changed by 45±201g and 3.2 ±3.6kg, whereas the BFR group changed by 21±103g and 2.6±3.9kg. The TEMS group changed by -37±121 g and 0.7±3.0kg; while the CON group changed by and 4±78g and 0.6±2.9kg. There was no significant difference between groups for maximal strength (p=0.2) or size (p=0.4). LEG: Leg strength changed by 32±19 kg in the COMB group and was significantly different than the 3±11kg change in the CON group (p=0.03). The TEMS and BFR group changed by 16±28kg and 18±17kg, respectively. There were no other significant differences between groups. Leg size changed by 95±238g in the COMB group; whereas size changed by 79±439g and 26±387g in the TEMS and BFR groups, respectively. The CON group changed by -83±279g. There were no significant differences between groups for leg size. The results suggest no effect of the intervention, however, despite a relative lack of overall statistical significance, owing to large individual variability in response, there is an indication of a possible effect. The absence of significance in most comparisons is likely explicable by the high inter-individual variability and differential adaptive responses

Reconfigurable photonic logic architecture

The amorphous silicon photo-sensor studied in this thesis, is a double pin structure (p(a-SiC:H)-i’(a-SiC:H)-n(a-SiC:H)-p(a-SiC:H)-i(a-Si:H)-n(a-Si:H)) sandwiched between two transparent contacts deposited over transparent glass thus with the possibility of illumination on both sides, responding to wave-lengths from the ultra-violet, visible to the near infrared range. The frontal il-lumination surface, glass side, is used for light signal inputs. Both surfaces are used for optical bias, which changes the dynamic characteristics of the photo-sensor resulting in different outputs for the same input. Experimental studies were made with the photo-sensor to evaluate its applicability in multiplexing and demultiplexing several data communication channels. The digital light sig-nal was defined to implement simple logical operations like the NOT, AND, OR, and complex like the XOR, MAJ, full-adder and memory effect. A pro-grammable pattern emission system was built and also those for the validation and recovery of the obtained signals. This photo-sensor has applications in op-tical communications with several wavelengths, as a wavelength detector and to execute directly logical operations over digital light input signals

Motor imagery-based brain-computer interface by implementing a frequency band selection

Les interfícies cervell-ordinador basades en imaginacions motores (MI-BCI) són una promesa per a revolucionar la manera com els humans interactuen amb les màquines o el programari, realitzant accions només amb el pensament. Els pacients que pateixen discapacitats de moviment crítiques, com l'esclerosi lateral amiotròfica (ALS) o la tetraplegia, podrien utilitzar aquesta tecnologia per controlar una cadira de rodes, pròtesis robòtiques o qualsevol altre dispositiu que els permeti interactuar de manera independent amb el seu entorn. L'objectiu d'aquest projecte és ajudar les comunitats afectades per aquests trastorns amb el desenvolupament d'un mètode que sigui capaç de detectar, amb la màxima precisió possible, la intenció d'executar moviments (sense que es produeixin) en les extremitats superiors del cos. Això es farà mitjançant senyals adquirits amb un electroencefalograma (EEG), el seu condicionament i processament, i la seva posterior classificació amb models d'intel·ligència artificial. A més, es dissenyarà un filtre de senyal digital per mantenir les bandes de freqüència més característiques de cada individu i augmentar significativament l’exactitud del sistema. Després d'extreure les característiques estadístiques, freqüencials i espacials més discriminatòries, va ser possible obtenir una exactitud del 88% en les dades de validació a l'hora de detectar si un participant estava imaginant un moviment de la mà esquerra o de la dreta. A més, es va utilitzar una xarxa neuronal convolucional (CNN) per distingir si el participant estava imaginant un moviment o no, la qual cosa va aconseguir una exactitud del 78% i una precisió del 90%. Aquests resultats es verificaran mitjançant la implementació d'una simulació en temps real amb l'ús d'un braç robòtic.Las interfaces cerebro-computadora basadas en imaginaciones motoras (MI-BCI) son una promesa para revolucionar la forma en que los humanos interactúan con las máquinas o el software, realizando acciones con tan solo pensar en ellas. Los pacientes que sufren discapacidades críticas del movimiento, como la esclerosis lateral amiotrófica (ALS) o la tetraplejia, podrían usar esta tecnología para controlar una silla de ruedas, prótesis robóticas o cualquier otro dispositivo que les permita interactuar de manera independiente con su entorno. El objetivo de este proyecto es ayudar a las comunidades afectadas por estos trastornos con el desarrollo de un método que sea capaz de detectar, con la mayor precisión posible, la intención de ejecutar movimientos (sin que se produzcan) en las extremidades superiores del cuerpo. Esto se hará mediante señales adquiridas con un electroencefalograma (EEG), su acondicionamiento y procesamiento, y su posterior clasificación con modelos de inteligencia artificial. Además, se diseñará un filtro de señal digital para mantener las bandas de frecuencia más características de cada individuo y aumentar significativamente la exactitud del sistema. Después de extraer características estadísticas, frecuenciales y espaciales discriminatorias, fue posible obtener una exactitud del 88% en los datos de validación a la hora de detectar si un participante estaba imaginando un movimiento con la mano izquierda o con la derecha. Además, se utilizó una red neural convolucional (CNN) para distinguir si el participante estaba imaginando un movimiento o no, lo que logró un 78% de exactitud y un 90% de precisión. Estos resultados se verificarán implementando una simulación en tiempo real con el uso de un brazo robótico.Motor Imagery-based Brain-Computer Interfaces (MI-BCI) are a promise to revolutionize the way humans interact with machinery or software, performing actions by just thinking about them. Patients suffering from critical movement disabilities, such as amyotrophic lateral sclerosis (ALS) or tetraplegia, could use this technology to control a wheelchair, robotic prostheses, or any other device that could let them interact independently with their surroundings. The focus of this project is to aid communities affected by these disorders with the development of a method that is capable of detecting, as accurately as possible, the intention to execute movements (without them occurring) in the upper extremities of the body. This will be done through signals acquired with an electroencephalogram (EEG), their conditioning and processing, and their subsequent classification with artificial intelligence models. In addition, a digital signal filter will be designed to keep the most characteristic frequency bands of each individual and increase accuracy significantly. After extracting discriminative statistical, frequential, and spatial features, it was possible to obtain an 88% accuracy on validation data when it came to detecting whether a participant was imagining a left-hand or a right-hand movement. Furthermore, a Convolutional Neural Network (CNN) was used to distinguish if the participant was imagining a movement or not, which achieved a 78% accuracy and a 90% precision. These results will be verified by implementing a real-time simulation with the usage of a robotic arm

The potential of error-related potentials. Analysis and decoding for control, neuro-rehabilitation and motor substitution

Las interfaces cerebro-máquina (BMIs, por sus siglas en inglés) permiten la decodificación de patrones de activación neuronal del cerebro de los usuarios para proporcionar a personas con movilidad severamente limitada, ya sea debido a un accidente o a una enfermedad neurodegenerativa, una forma de establecer una conexión directa entre su cerebro y un dispositivo. En este sentido, las BMIs basadas en técnicas no invasivas, como el electroencefalograma (EEG) han ofrecido a estos usuarios nuevas oportunidades para recuperar el control sobre las actividades de su vida diaria que de otro modo no podrían realizar, especialmente en las áreas de comunicación y control de su entorno.En los últimos años, la tecnología está avanzando a grandes pasos y con ella la complejidad de dispositivos ha incrementado significativamente, ampliando el número de posibilidades para controlar sofisticados dispositivos robóticos, prótesis con numerosos grados de libertad o incluso para la aplicación de complejos patrones de estimulación eléctrica en las propias extremidades paralizadas de un usuario, que le permitan ejecutar movimientos precisos. Sin embargo, la cantidad de información que se puede transmitir entre el cerebro y estos dispositivos sigue siendo muy limitada, tanto por el número como por la velocidad a la que se pueden decodificar los comandos neuronales. Por lo tanto, depender únicamente de las señales neuronales no garantiza un control óptimo y preciso.Para poder sacar el máximo partido de estas tecnologías, el campo de las BMIs adoptó el conocido enfoque de “control-compartido". Esta estrategia de control pretende crear un sistema de cooperación entre el usuario y un dispositivo inteligente, liberando al usuario de las tareas más pesadas requeridas para ejecutar la tarea sin llegar a perder la sensación de estar en control. De esta manera, los usuarios solo necesitan centrar su atención en los comandos de alto nivel (por ejemplo, elegir un elemento específico que agarrar, o elegir el destino final donde moverse) mientras el agente inteligente resuelve problemas de bajo nivel (como planificación de trayectorias, esquivar obstáculos, etc.) que permitan realizar la tarea designada de la manera óptima.En particular, esta tesis gira en torno a una señal neuronal cognitiva de alto nivel originada como la falta de coincidencia entre las expectativas del usuario y las acciones reales ejecutadas por los dispositivos inteligentes. Estas señales, denominadas potenciales de error (ErrPs), se consideran una forma natural de intercomunicar nuestro cerebro con máquinas y, por lo tanto, los usuarios solo requieren monitorizar las acciones de un dispositivo y evaluar mentalmente si este último se comporta correctamente o no. Esto puede verse como una forma de supervisar el comportamiento del dispositivo, en el que la decodificación de estas evaluaciones mentales se utiliza para proporcionar a estos dispositivos retroalimentación directamente relacionada con la ejecución de una tarea determinada para que puedan aprender y adaptarse a las preferencias del usuario.Dado que la respuesta neuronal de ErrP está asociada a un evento exógeno (dispositivo que comete una acción errónea), la mayoría de los trabajos desarrollados han intentado distinguir si una acción es correcta o errónea mediante la explotación de eventos discretos en escenarios bien controlados. Esta tesis presenta el primer intento de cambiar hacia configuraciones asíncronas que se centran en tareas relacionadas con el aumento de las capacidades motoras, con el objetivo de desarrollar interfaces para usuarios con movilidad limitada. En este tipo de configuraciones, dos desafíos importantes son que los eventos correctos o erróneos no están claramente definidos y los usuarios tienen que evaluar continuamente la tarea ejecutada, mientras que la clasificación de las señales EEG debe realizarse de forma asíncrona. Como resultado, los decodificadores tienen que lidiar constantemente con la actividad EEG de fondo, que típicamente conduce a una gran cantidad de errores de detección de firmas de error. Para superar estos desafíos, esta tesis aborda dos líneas principales de trabajo.Primero, explora la neurofisiología de las señales neuronales evocadas asociadas con la percepción de errores durante el uso interactivo de un BMI en escenarios continuos y más realistas.Se realizaron dos estudios para encontrar características alternativas basadas en el dominio de la frecuencia como una forma de lidiar con la alta variabilidad de las señales del EEG. Resultados, revelaron que existe un patrón estable representado como oscilaciones "theta" que mejoran la generalización durante la clasificación. Además, se utilizaron técnicas de aprendizaje automático de última generación para aplicar el aprendizaje de transferencia para discriminar asincrónicamente los errores cuando se introdujeron de forma gradual y no se conoce presumiblemente el inicio que desencadena los ErrPs. Además, los análisis de neurofisiología arrojan algo de luz sobre los mecanismos cognitivos subyacentes que provocan ErrP durante las tareas continuas, lo que sugiere la existencia de modelos neuronales en nuestro cerebro que acumulan evidencia y solo toman una decisión al alcanzar un cierto umbral. En segundo lugar, esta tesis evalúa la implementación de estos potenciales relacionados con errores en tres aplicaciones orientadas al usuario. Estos estudios no solo exploran cómo maximizar el rendimiento de decodificación de las firmas ErrP, sino que también investigan los mecanismos neuronales subyacentes y cómo los diferentes factores afectan las señales provocadas.La primera aplicación de esta tesis presenta una nueva forma de guiar a un robot móvil que se mueve en un entorno continuo utilizando solo potenciales de error como retroalimentación que podrían usarse para el control directo de dispositivos de asistencia. Con este propósito, proponemos un algoritmo basado en el emparejamiento de políticas para el aprendizaje de refuerzo inverso para inferir el objetivo del usuario a partir de señales cerebrales.La segunda aplicación presentada en esta tesis contempla los primeros pasos hacia un BCI híbrido para ejecutar distintos tipos de agarre de objetos, con el objetivo de ayudar a las personas que han perdido la funcionalidad motora de su extremidad superior. Este BMI combina la decodificación del tipo de agarre a partir de señales de EEG obtenidas del espectro de baja frecuencia con los potenciales de error provocados como resultado de la monitorización de movimientos de agarre erróneos. Los resultados muestran que, en efecto los ErrP aparecen en combinaciones de señales motoras originadas a partir de movimientos de agarre consistentes en una única repetición. Además, la evaluación de los diferentes factores involucrados en el diseño de la interfaz híbrida (como la velocidad de los estímulos, el tipo de agarre o la tarea mental) muestra cómo dichos factores afectan la morfología del subsiguiente potencial de error evocado.La tercera aplicación investiga los correlatos neuronales y los procesos cognitivos subyacentes asociados con desajustes somatosensoriales producidos por perturbaciones inesperadas durante la estimulación eléctrica neuromuscular en el brazo de un usuario. Este estudio simula los posibles errores que ocurren durante la terapia de neuro-rehabilitación, en la que la activación simultánea de la estimulación aferente mientras los sujetos se concentran en la realización de una tarea motora es crucial para una recuperación óptima. Los resultados muestran que los errores pueden aumentar la atención del sujeto en la tarea y desencadenar mecanismos de aprendizaje que al mismo tiempo podrían promover la neuroplasticidad motora.En resumen, a lo largo de esta tesis, se han diseñado varios paradigmas experimentales para mejorar la comprensión de cómo se generan los potenciales relacionados con errores durante el uso interactivo de BMI en aplicaciones orientadas al usuario. Se han propuesto diferentes métodos para pasar de la configuración bloqueada en el tiempo a la asíncrona, tanto en términos de decodificación como de percepción de los eventos erróneos; y ha explorado tres aplicaciones relacionadas con el aumento de las capacidades motoras, en las cuales los ErrPs se pueden usar para el control de dispositivos, la sustitución de motores y la neuro-rehabilitación.Brain-machine interfaces (BMIs) allow the decoding of cortical activation patterns from the users brain to provide people with severely limited mobility, due to an accident or disease, a way to establish a direct connection between their brain and a device. In this sense, BMIs based in noninvasive recordings, such as the electroencephalogram (EEG) have o↵ered these users new opportunities to regain control over activities of their daily life that they could not perform otherwise, especially in the areas of communication and control of their environment. Over the past years and with the latest technological advancements, devices have significantly grown on complexity expanding the number of possibilities to control complex robotic devices, prosthesis with numerous degrees of freedom or even to apply compound patterns of electrical stimulation on the subjects own paralyzed extremities to execute precise movements. However, the band-with of communication between brain and devices is still very limited, both in terms of the number and the speed at which neural commands can be decoded, and thus solely relying on neural signals do not guarantee accurate control them. In order to benefit of these technologies, the field of BMIs adopted the well-known approach of shared-control. This strategy intends to create a cooperation system between the user and an intelligent device, liberating the user from the burdensome parts of the task without losing the feeling of being in control. Here, users only need to focus their attention on high-level commands (e.g. choose the final destination to reach, or a specific item to grab) while the intelligent agent resolve low-level problems (e.g. trajectory planning, obstacle avoidance, etc) to perform the designated task in the optimal way. In particular, this thesis revolves around a high-level cognitive neural signal originated as the mismatch between the expectations of the user and the actual actions executed by the intelligent devices. These signals, denoted as error-related potentials (ErrPs), are thought as a natural way to intercommunicate our brain with machines and thus users only require to monitor the actions of a device and mentally assess whether the latter is behaving correctly or not. This can be seen as a way to supervise the device’s behavior, in which the decoding of these mental assessments is used to provide these devices with feedback directly related with the performance of a given task so they can learn and adapt to the user’s preferences. Since the ErrP’s neural response is associated to an exogenous event (device committing an erroneous action), most of the developed works have attempted to distinguish whether an action is correct or erroneous by exploiting discrete events under well-controlled scenarios. This thesis presents the first attempt to shift towards asynchronous settings that focus on tasks related with the augmentation of motor capabilities, with the objective of developing interfaces for users with limited mobility. In this type of setups, two important challenges are that correct or erroneous events are not clearly defined and users have to continuously evaluate the executed task, while classification of EEG signals has to be performed asynchronously. As a result, the decoders have to constantly deal with background EEG activity, which typically leads to a large number of missdetection of error signatures. To overcome these challenges, this thesis addresses two main lines of work. First, it explores the neurophysiology of the evoked neural signatures associated with the perception of errors during the interactive use of a BMI in continuous and more realistic scenarios. Two studies were performed to find alternative features based on the frequency domain as a way of dealing with the high variability of EEG signals. Results, revealed that there exists a stable pattern represented as theta oscillations that enhance generalization during classification. Also, state-of-the-art machine learning techniques were used to apply transfer learning to asynchronously discriminate errors when they were introduced in a gradual fashion and the onset that triggers the ErrPs is not presumably known. Furthermore, neurophsysiology analyses shed some light about the underlying cognitive mechanisms that elicit ErrP during continuous tasks, suggesting the existence of neural models in our brain that accumulate evidence and only take a decision upon reaching a certain threshold. Secondly, this thesis evaluates the implementation of these error-related potentials in three user-oriented applications. These studies not only explore how to maximize the decoding performance of ErrP signatures but also investigate the underlying neural mechanisms and how di↵erent factors a↵ect the elicited signals. The first application of this thesis presents a new way to guide a mobile robot moving in a continuous environment using only error potentials as feedback which could be used for the direct control of assistive devices. With this purpose, we propose an algorithm based on policy matching for inverse reinforcement learning to infer the user goal from brain signals. The second application presented in this thesis contemplates the first steps towards a hybrid BMI for grasping oriented to assist people who have lost motor functionality of their upper-limb. This BMI combines the decoding of the type of grasp from low-frequency EEG signals with error-related potentials elicited as the result of monitoring an erroneous grasping. The results show that ErrPs are elicited in combination of motor signatures from the low-frequency spectrum originated from single repetition grasping tasks and evaluates how di↵erent design factors (such as the speed of the stimuli, type of grasp or mental task) impact the morphology of the subsequent evoked ErrP. The third application investigates the neural correlates and the underlying cognitive processes associated with somatosensory mismatches produced by unexpected disturbances during neuromsucular electrical stimulation on a user’s arm. This study simulates possible errors that occur during neurorehabilitation therapy, in which the simultaneous activation of a↵erent stimulation while the subjects are concentrated in performing a motor task is crucial for optimal recovery. The results showed that errors may increase subject’s attention on the task and trigger learning mechanisms that at the same time could promote motor neuroplasticity. In summary, throughout this thesis, several experimental paradigms have been designed to improve the understanding of how error-related potentials are generated during the interactive use of BMIs in user-oriented applications. Di↵erent methods have been proposed to shift from time-locked to asynchronous settings, both in terms of decoding and perception of the erroneous events; and it has explored three applications related with the augmentation of motor capabilities, in which ErrPs can be used for control of devices, motor substitution and neurorehabilitation.<br /

P300 wave detection using Emotiv EPOC+ headset: effects of matrix size, flash duration, and colors

Includes bibliographical references.2016 Fall.Brain-computer interfaces (BCIs) allow interactions between human beings and comput- ers without using voluntary muscle. Enormous research effort has been employed in the last few decades to design convenient and user-friendly interfaces. The aim of this study is to provide the people with severe neuromuscular disorders a new augmentative communication technology so that they can express their wishes and communicate with others. The research investigates the capability of Emotiv EPOC+ headset to capture and record one of the BCIs signals called P300 that is used in several applications such as the P300 speller. The P300 speller is a BCI system used to enable severely disabled people to spell words and convey their thoughts without any physical effort. In this thesis, the effects of matrix size, flash duration, and colors were studied. Data are collected from five healthy subjects in their home environments. Different programs are used in this experiment such as OpenViBE platform and MATLAB to pre-process and classify the EEG data. Moreover, the Linear Discriminate Analysis (LDA) classification algorithm is used to classify the data into target and non-target samples

THE CHIME: POETICALLY TRANSLATING THE DISCRETE DIFFERENCE OF AGNSOTIC SENSORS INTO A SONIFICATION OF THE CITY

Inspired by Georg Simmel’s notion of the blasé and Mark Weiser’s vision for calm technology, this document detailing the application of critical concepts to the realization of a design intention is a critical and creative exploration of computation and the everyday. While paying particular attention to the conceptual underappreciation of acoustic space and place, I outline a case for poetically translating data collected from inherently agnostic sensors through the design, construction and use of an instrument for sensing environmental difference (comprised of 18 sensors measuring 27 data points) and exemplified through a musical sonification. A generative instrument, such as The Chime, takes external impulses and translates them poetically into a form that naturally casts the attention back upon the initial gust. In the built environment such treatment of discrete sensing could help engender what I call acoustic places; place that, even if for a passing moment, might resonate harmonically and reciprocally with the inspiration for its emission

Wireless sensor networks for medical care.

Chen, Xijun.Thesis (M.Phil.)--Chinese University of Hong Kong, 2008.Includes bibliographical references (leaves 72-77).Abstracts in English and Chinese.Chapter Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Design Challenges --- p.2Chapter 1.2 --- Wireless Sensor Network Applications --- p.6Chapter 1.2.1 --- Military Applications --- p.7Chapter 1.2.2 --- Environmental Applications --- p.9Chapter 1.2.3 --- Health Applications --- p.11Chapter 1.3 --- Wireless Biomedical Sensor Networks (WBSN) --- p.12Chapter 1.4 --- Text Organization --- p.13Chapter Chapter 2 --- Design a Wearable Platform for Wireless Biomedical Sensor Networks --- p.15Chapter 2.1 --- Objective --- p.17Chapter 2.2 --- Requirements for Wireless Medical Sensors --- p.19Chapter 2.3 --- Hardware design --- p.21Chapter 2.3.1 --- Materials and Methods --- p.21Chapter 2.3.2 --- Results --- p.24Chapter 2.3.3 --- Conclusion --- p.27Chapter 2.4 --- Software design --- p.28Chapter 2.4.1 --- TinyOS --- p.28Chapter 2.4.2 --- Software Organization --- p.28Chapter Chapter 3 --- Wireless Medical Sensors --- p.32Chapter 3.1 --- Sensing Physiological Information --- p.32Chapter 3.1.1 --- Pulse Oximetry --- p.32Chapter 3.1.2 --- Electrocardiograph --- p.36Chapter 3.1.3 --- Galvanic Skin Response --- p.41Chapter 3.2 --- Location Tracking --- p.43Chapter 3.2.1 --- Outdoor Location Tracking --- p.43Chapter 3.2.2 --- Indoor Location Tracking --- p.44Chapter 3.3 --- Motion Tracking --- p.49Chapter 3.3.1 --- Technology --- p.50Chapter 3.3.2 --- Motion Analysis Sensor Board --- p.51Chapter 3.4 --- Discussions --- p.52Chapter Chapter 4 --- Applications in Medical Care --- p.54Chapter 4.1 --- Introduction --- p.54Chapter 4.2 --- Wearable Wireless Body Area Network --- p.56Chapter 4.2.1 --- Architecture --- p.58Chapter 4.2.2 --- Deployment Scenarios --- p.62Chapter 4.3 --- Application in Ambulatory Setting --- p.63Chapter 4.3.1 --- Method --- p.64Chapter 4.3.2 --- The Software Architecture --- p.66Chapter Chapter 5 --- Conclusions and Future Work --- p.69References --- p.72Appendix --- p.7