Made available in DSpace on 2014-12-17T14:56:05Z (GMT). No. of bitstreams: 1 AndreFB_DISSERT.pdf: 2147554 bytes, checksum: 3ed5f0d06e3b072597f2eae69b7d1ca2 (MD5) Previous issue date: 2012-02-24 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior / The Brain-Computer Interfaces (BCI) have as main purpose to establish a communication path with the central nervous system (CNS) independently from the standard pathway (nervous, muscles), aiming to control a device. The main objective of the current research is to develop an off-line BCI that separates the different EEG patterns resulting from strictly mental tasks performed by an experimental subject, comparing the effectiveness of different signal-preprocessing approaches. We also tested different classification approaches: all versus all, one versus one and a hierarchic classification approach. No preprocessing techniques were found able to improve the system performance. Furthermore, the hierarchic approach proved to be capable to produce results above the expected by literature / As interfaces c?rebro-computador (ICC) t?m como objetivo estabelecer uma via de comunica??o com o sistema nervoso central (SNC) que seja independente das vias padr?o (nervos, m?sculos), visando o controle de algum dispositivo. O objetivo principal da presente pesquisa ? desenvolver uma ICC off-line que separe os diferentes padr?es de EEG resultantes de tarefas puramente mentais realizadas por um sujeito experimental, comparando a efic?cia de diferentes abordagens de pr?-processamento do sinal. Tamb?m foram testadas diferentes abordagens de classifica??o: todos contra todos, um contra um e uma abordagem hier?rquica de classifica??o. N?o foram encontradas t?cnicas de pr?-processamento que melhorem os resultados do sistema. Al?m disso, a abordagem hier?rquica sugerida mostrou-se capaz de produzir resultados acima do padr?o esperado pela literatura

Orientador: Prof. Dr. Diogo Coutinho Soriano / Dissertação (mestrado) - Universidade Federal do ABC. Programa de Pós-Graduação em Engenharia Biomédica, 2018. / A busca por materiais funcionais que possam desempenhar reparo e/ou regeneracao de Uma interface cérebro-computador (BCI) consiste em um sistema que busca extrair informações da atividade do sistema nervoso central e traduzi-las em comandos de saída, os quais podem eventualmente ser usados para controle de dispositivos assistivos. Mais do que contribuir para o controle de tecnologias assistivas ou reabilitação de pessoas com severas limitações, um sistema BCI pode contribuir para uma melhor compreensão do funcionamento cerebral e dos complexos mecanismos de cognição na medida em que se busca avaliar as variáveis mais relevantes para a eficiente decodificação de tarefas mentais. Entre as possíveis formas de se estudar o funcionamento cerebral destaca-se a quantificação da conectividade funcional, a qual visa estabelecer a similaridade observacional entre diferentes regiões cerebrais. Tal estratégia tem sido utilizada na caracterização e diagnóstico de patologias de grande relevância como depressão, Parkinson, Alzheimer, distúrbios de atenção, entre outras. Tendo isso em vista, este trabalho visou estudar o desempenho de decodificação de tarefas mentais a partir de métricas de grafos (grau, coeficiente de agregação, centralidade de intermediação e centralidade de autovetor) obtidas pela avaliação da conectividade funcional no contexto de sinais eletroencefalográficos na execução de paradigmas clássicos de sistemas BCI definidos pela imagética motora e os potenciais visualmente evocados em regime permanente (SSVEP). Além da análise comparativa entre tais métricas, o presente trabalho apresenta um estudo em relação ao desempenho de decodificação quando diferentes métodos de estimação da matriz de adjacência - forma de representação da conectividade funcional ¿ são utilizados, os quais abrangem as medidas de similaridade definidas pela correlação de Pearson, de Spearman e contagem de recorrência espaço-temporal (STR), sendo a última uma proposta original desta dissertação. Como resultado, para os sinais relacionados à BCIs baseadas em imaginação de movimentos, a STR obteve o melhor desempenho considerando todos os sujeitos e classes, mostrando-se uma possível abordagem para extração de características no contexto de sistemas BCI baseadas em imagética de tarefas. Para os sinais relacionados ao paradigma SSVEP, a decodificação baseada na conectividade funcional alcançou desempenhos satisfatórios, porém inferiores aos da análise em frequência classicamente utilizada neste contexto. / Brain-computer interface (BCI) consists of a system that aims to extract information from the activity of central nervous system and translate it into output commands, which can eventually be used to control assistive devices. More than contributing to the control of assistive technologies or rehabilitation of people with severe limitations, a BCI can also contribute to a better understanding of brain functioning and the complex mechanisms of cognition when evaluating the most relevant variables for the efficient decoding of mental tasks. Among the possible ways to study brain functioning, the functional connectivity quantification deserves careful attention, since it aims to establish the observational similarity between different brain regions. Such strategy has been used in the characterization and diagnosis of pathologies of great relevance such as depression, Parkinson, Alzheimer, attention disorders, among others. This work aimed to study the performance of decoding mental tasks from graph metrics (degree, clustering coefficient, betweenness centrality and eigenvector centrality) obtained by evaluation of functional connectivity in the context of electroencephalographic signals in the execution of classic BCI paradigms defined by motor imagery and steady state visually evoked potentials (SSVEP). In addition to the comparative analysis of such metrics, this work also presents a study regarding decoding performance when using different methods of adjacency matrix estimation - a functional connectivity representation - which include similarity measures defined by the correlation of Pearson, Spearman and Space-Time Recurrence counting (STR), being the latter an original proposal of this work. As main results, for signals related to motor imagery BCI, STR obtained the best performance considering all the subjects and classes, stablishing a possible approach for feature extraction in the context of motor imagery BCIs. For signals related to the SSVEP paradigm, decoding based on functional connectivity achieved satisfactory performance, but lower than the spectral analysis, classically used in this context.

Orientador: Prof. Dr. Ricardo Suyama / Dissertação (mestrado) - Universidade Federal do ABC, Programa de Pós-Graduação em Engenharia da Informação, Santo André, 2018. / Uma interface cerebro-computador (BCI) e projetada para que se consiga, de modo efetivo, fornecer uma via alternativa de comunicacao entre o cerebro do usuario e o computador. Sinais captados por meio de eletrodos, tipicamente posicionados no escalpo do individuo, sao previamente processados para que haja eliminacao de ruidos externos. A partir dai, diversas tecnicas para processamento de sinais sao utilizadas para posteriormente classificar os sinais registrados e realizar a traducao do estado mental do usuario em um comando especifico a ser executado pelo computador. No presente trabalho sao utilizadas tecnicas de representacao esparsa dos sinais para a extracao de caracteristicas relevantes para classificacao dos mesmos, com intuito de aumentar a robustez e melhorar o desempenho do sistema. Para a extracao de sinais esparsos, foram utilizados algoritmos de criacao de dicionarios, a partir dos quais e possivel obter uma representacao esparsa para todo o subespaco de sinal. No trabalho foram utilizados 5 diferentes algoritmos de criacao de dicionario: Metodo de direcoes otimas (MOD), K-SVD, RLS-DLA, LS-DLA e Aprendizado de dicionario Online (ODL). A classificacao dos sinais foi realizada com o metodo de .. vizinhos mais proximos (k - NN). Os resultados obtidos com a abordagem de representacao esparsa foram comparados com os resultados do BCI Competition IV dataset 2a. Para o primeiro colocado da competicao foi obtido, em termos do coeficiente kappa, uma acuracia de 0.57 enquanto que no trabalho utilizando os metodos esparsos, obteve-se, em coeficiente kappa, uma acuracia de 0.90. Em comparacao obteve-se um ganho de 0.33 de acuracia, onde se deduz que o uso de sinais esparsos pode ser benefico para o dificil problema de se projetar uma interface cerebro computador. / A brain computer interface (BCI) is designed to effectively translate commands thought by human individuals into commands that a computer can effectively understand. Electrical impulses generated from the brain sculp are recorded from a device called an electroencephalograph and are preprocessed for elimination of external noise. From there, several techniques for signal processing are used to later classify the signals obtained by the electroencephalograph. In this work, techniques for sparse representation of signals are used for feature extraction, in order to increase robustness and system performance. For the extraction of sparse signals, five different dictionary learning algorithms were used, being able to produce a basis capable of represensing the entire signal subspace. In this work, 5 different dictionary learning algorithms were used: Method of Optimal Directions (MOD), K-SVD, Recursive Least Square Dictionary Learning (RLS-DLA), Least Square Dictionary Learning (LS-DLA) and Online Dictionary Learning (ODL). For the classification task, the k-NN method was used. The simulation results obtained with this approach were compared with the best BCI Competition IV dataset 2a results. For the first place in the competition, an accuracy of 0.57 was obtained, in terms of the kappa coefficient, whereas in the work using the sparse methods, a kappa coefficient of 0.90 was obtainned, improving accuracy in 0.33 accuracy was obtained, which indicates that the use of sparse signals may be beneficial to the difficult problem of designing a brain computer interface.

Orientador: Maria Cecilia Calani Baranauskas / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Computação / Made available in DSpace on 2018-08-12T08:59:53Z (GMT). No. of bitstreams: 1 Hornung_HeikoHorst_M.pdf: 2237274 bytes, checksum: d8329b2e979c07802514aa9b26b8b128 (MD5) Previous issue date: 2008 / Resumo: Serviços do governo eletrônico (eGov, do inglês: electronic government/e-government) são um veículo de comunicação entre as entidades do governo nos diferentes níveis (municipal, estadual, etc.) e os cidadãos. Além de tornar ações do governo mais transparentes e aumentar a eficiência e eficácia, esses serviços visam fortalecer a democracia oferecendo a possibilidade de participação dos cidadãos nos processos democráticos. Para tais fins, serviços de eGov precisam possibilitar o acesso pela população inteira, isto é para pessoas com diferentes competências ou necessidades específicas. A contribuição desse trabalho envolve mostrar caminhos para como interfaces de usuário de serviços de eGov podem ser projetados de uma maneira inclusiva, respeitando a diversidade de uma população. Partindo de uma análise do contexto brasileiro, esse trabalho mostra tanto as principais diferenças entre serviços de eGov e outras aplicações web quanto as diferenças entre países em desenvolvimento e países desenvolvidos a esse respeito. O principal desafio identificado é a adaptaçãao de métodos tradicionais ao contexto de serviços inclusivos de eGov. No próximo passo identificamos barreiras do acesso ao serviços de eGov por usuários com necessidades específicas como diferentes de eficiências, baixo letramento ou baixo letramento digital. Propomos o conceito de técnicas assistivas" que ampliam a visão limitada de tecnologias assistivas para o contexto de nosso cenário, isto é, um uso por pessoas que usam serviços em diferentes situações, inclusive de eficiências. Os desafios identificados e diferentes experiências trazidas de projetos nos motivaram a propor um framework para o design socialmente responsável. Os elementos principais desse framework são métodos e técnicas da Semiótica Organizacional e do Design Participativo para atingir uma visão sócio-tecnica dos problemas de design. Esses métodos e técnicas são aplicados em Práticas Participativas Inclusivas em um Cenário*, um grupo de representantes de usuários finais que foram escolhidos como imagem de características encontradas na sociedade brasileira. Por fim analisamos um conjunto de protótipos que foram criados dentro do contexto do framework de design socialmente responsável. Como o design de serviços inclusivos de eGov depende de fatores culturais entre outros, criamos um design rationale abstrato que discute diferentes questões de design e assim visa apoiar o designer na tomada de decisões adequadas ao respectivo contexto. / Abstract: Electronic government (eGov) services are means of communication between entities of the government (on local, state or other levels) and the citizens. Besides making actions of the government more transparent and increasing efficiency and effectiveness, such servicees aim to strengthen democracy by offering citizens possibilities to participate in democratic processes. Thus, eGov services have to enable access to the whole population, including people with different competencies or special needs. The contribution of this work involves showing ways of creating user interfaces to eGov services inclusively and respecting the diversity of the population. Starting with an analysis of the Brazilian country context, this work shows the main differences between eGov services and other web applications as well as differences between developing and developed countries regarding those applications. The principal challenge that has been identified is that of adapting traditional methods to the context of inclusive eGov services. In the next step we identify barriers of access to eGov services that are imposed on users with special needs like impairments, low literacy or low digital literacy. We propose the concept of \assistive techniques" to extend the limited vision of assistive technologies to the context of our scenario, i.e. to people with special needs besides impairments who make use of eGov services in different situations. The challenges identified and different experiences from diffrent projects motivated us to propose a framework for socially responsible design. The main elements of this framework are methods and techniques from Organizational Semiotics and Participatory Design in order to get a socio-technical vision of design problems. These methods and techniques are employed during Participatory Inclusive Practices in a Cenário*, a group of end user representatives that has been composed to mirror the characteristics of the Brazilian society. Finally, we analyze a set of prototypes that have been created within the context of the framework of socially responsible design. Since the design of inclusive eGov services depends on cultural and other factors, we created an abstract design rationale that discusses different design issues and thus supports the designer in taking decisions that are tailored to the respective context. / Mestrado / Mestre em Ciência da Computação

Eye-tracking devices can tell us where on the screen a person is looking. Researchers frequently analyze eye-tracking data manually, by examining every frame of a visual stimulus used in an eye-tracking experiment so as to match 2D screen-coordinates provided by the eye-tracker to related objects and content within the stimulus. Such task requires significant manual effort and is not feasible for analyzing data collected from many users, long experimental sessions, and heavily interactive and dynamic visual stimuli. In this dissertation, we present a novel analysis method. We would instrument visualizations that have open source code, and leverage real-time information about the layout of the rendered visual content, to automatically relate gaze-samples to visual objects drawn on the screen. Since such visual objects are shown in a visualization stand for data, the method would allow us to necessarily detect data that users focus on or Data of Interest (DOI). This dissertation has two contributions. First, we demonstrated the feasibility of collecting DOI data for real life visualization in a reliable way which is not self-evident. Second, we formalized the process of collecting and interpreting DOI data and test whether the automated DOI detection can lead to research workflows, and insights not possible with traditional, manual approaches.

Physiotherapy patients, victims of accidents, strokes and injuries are submitted for several months of repetitive exercise in rehabilitation sessions, depending almost entirely of aid and monitoring of the physical therapist. The performed exercises should be supervised and evaluated regularly in order to measure the patient's progress in treatment. The evolution of new interaction technologies with the patient, through low-cost motion detection sensors, enables the creation for physical therapy support software. Furthermore, the introduction of Exergames and BrainTraining games in the fields of medicine and physical therapy has shown that serious games can be used to assist the treatment of diseases and patients in special conditions. However, few studies demonstrate the importance or relevant results regarding the usability of solutions. This paper presents the development and evaluation of a gaming system to assist physiotherapists in quality analysis and workout efficiency. The evaluation through specific questionnaires showed the quality of the developed system on the usability and functionality of the games. Also from the final collected data, it is presented an ideal standard for players movement data, that can be used as a reference during treatment of patients. / Pacientes de fisioterapia, vítimas de acidentes, derrames e lesões são submetidos por vários meses a sessões repetitivas de exercícios para reabilitação, dependendo quase que totalmente do auxílio e acompanhamento do fisioterapeuta. Os exercícios realizados devem ser supervisionados e avaliados regularmente, com o intuito de medir o progresso do paciente no tratamento. A evolução de novas tecnologias de interação com o paciente, através de sensores de detecção de movimentos de baixo custo, possibilita a criação de softwares de apoio ao tratamento fisioterapêutico. Além disso, a introdução de Exergames e BrainTraining games nos campos da medicina e fisioterapia tem demonstrado que é possível utilizar jogos sérios para auxiliar nos tratamentos de doenças e condições especiais dos pacientes. Contudo, poucos estudos demonstram a importância ou resultados relevantes quanto à usabilidade das soluções. Este trabalho apresenta o desenvolvimento e avaliação de um sistema de jogos para auxiliar fisioterapeutas na análise da qualidade e eficiência da sessão de exercícios. A avaliação realizada através de questionários específicos demonstrou a qualidade do sistema desenvolvido quanto à usabilidade e funcionalidade dos jogos apresentados. Também a partir dos dados finais coletados, apresenta-se um padrão ideal de dados para os movimentos dos jogadores, que pode ser usado como referência durante o tratamento de pacientes.

Gesture recognition is becoming increasingly popular as an input mechanism for human-computer interfaces. The availability of MEMS (Micro-Electromechanical System) 3-axis linear accelerometers allows for the design of an inexpensive mobile gesture recognition system. Wearable inertial sensors are a low-cost, low-power solution to recognize gestures and, more generally, track the movements of a person. Gesture recognition algorithms have traditionally only been implemented in cases where ample system resources are available, i.e. on desktop computers with fast processors and large amounts of memory. In the cases where a gesture recognition algorithm has been implemented on a resource-constrained device, only the simplest algorithms were implemented to recognize only a small set of gestures. Current gesture recognition technology can be improved by making algorithms faster, more robust, and more accurate. The most dramatic results in optimization are obtained by completely changing an algorithm to decrease the number of computations. Algorithms can also be optimized by profiling or timing the different sections of the algorithm to identify problem areas. Gestures have two aspects of signal characteristics that make them difficult to recognize: segmentation ambiguity and spatio-temporal variability. Segmentation ambiguity refers to not knowing the gesture boundaries, and therefore reference patterns have to be matched with all possible segments of input signals. Spatio-temporal variability refers to the fact that each repetition of the same gesture varies dynamically in shape and duration, even for the same gesturer. The objective of this study was to evaluate the various gesture recognition algorithms currently in use, after which the most suitable algorithm was optimized in order to implement it on a mobile device. Gesture recognition techniques studied include hidden Markov models, artificial neural networks and dynamic time warping. A dataset for evaluating the gesture recognition algorithms was gathered using a mobile device’s embedded accelerometer. The algorithms were evaluated based on computational efficiency, recognition accuracy and storage efficiency. The optimized algorithm was implemented in a user application on the mobile device to test the empirical validity of the study. / Dissertation (MEng)--University of Pretoria, 2009. / Electrical, Electronic and Computer Engineering / unrestricted

In an increasingly mobile world, the combination of mobile computing devices, publicly accessible Wi-Fi hotspots, and camera phones pose a significant threat to alphanumeric passwords in public environments. Graphical passwords, introduced as an alternative to alphanumerical passwords, help prevent successful shoulder-surfing attacks – covertly observing or recording a password login session, however, most cannot prevent intersection analysis on the data collected through shoulder-surfing. ColorDots is a new graphical password scheme designed to be easy to use and learn, to prevent successful shoulder-surfing attacks, and to hinder intersection analysis. A software implementation of ColorDots is tested, and the results analyzed. This study showed the ColorDots graphical password scheme does prevent shoulder-surfing, and hinders intersection analysis on digital recordings of multiple shoulder-surfing attacks. Furthermore, ColorDots may be just as convenient to use as alphanumeric passwords, while improving password security in public environments.

This thesis presents the design for a novel haptic interface for large-format touchscreens. Techniques such as electrovibration, ultrasonic vibration, and external braked devices have been developed by other researchers to deliver haptic feedback to touchscreen users. However, these methods do not address the need for spatial constraints that only restrict user motion in the direction of the constraint. This technology gap contributes to the lack of haptic technology available for touchscreen-based upper-limb rehabilitation, despite the prevalent use of haptics in other forms of robotic rehabilitation. The goal of this thesis is to display kinesthetic haptic constraints to the touchscreen user in the form of boundaries and paths, which assist or challenge the user in interacting with the touchscreen. The presented prototype accomplishes this by steering a single wheel in contact with the display while remaining driven by the user. It employs a novel embedded force sensor, which it uses to measure the interaction force between the user and the touchscreen. The haptic response of the device is controlled using this force data to characterize user intent. The prototype can operate in a simulated free mode as well as simulate rigid and compliant obstacles and path constraints. A data architecture has been created to allow the prototype to be used as a peripheral add-on device which reacts to haptic environments created and modified on the touchscreen. The long-term goal of this work is to create a haptic system that enables a touchscreen-based rehabilitation platform for people with upper limb impairments.

In augmented reality, information is often distributed between real and virtual contexts, and often appears at different distances from the viewer. This raises the issues of (1) context switching, when attention is switched between real and virtual contexts, (2) focal distance switching, when the eye accommodates to see information in sharp focus at a new distance, and (3) transient focal blur, when information is seen out of focus, during the time interval of focal distance switching. This dissertation research has quantified the impact of context switching, focal distance switching, and transient focal blur on human performance and eye fatigue in both monocular and binocular viewing conditions. Further, this research has developed a novel font that when seen out-of-focus looks sharper than standard fonts. This SharpView font promises to mitigate the effect of transient focal blur. Developing this font has required (1) mathematically modeling out-of-focus blur with Zernike polynomials, which model focal deficiencies of human vision, (2) developing a focus correction algorithm based on total variation optimization, which corrects out-of-focus blur, and (3) developing a novel algorithm for measuring font sharpness. Finally, this research has validated these fonts through simulation and optical camera-based measurement. This validation has shown that, when seen out of focus, SharpView fonts are as much as 40 to 50% sharper than standard fonts. This promises to improve font legibility in many applications of augmented reality.