Continuous sign recognition of brazilian sign language in a healthcare setting

Communication is the basis of human society. The majority of people communicate using spoken language in oral or written form. However, sign language is the primary mode of communication for deaf people. In general, understanding spoken information is a major challenge for the deaf and hard of hearing. Access to basic information and essential services is challenging for these individuals. For example, without translation support, carrying out simple tasks in a healthcare center such as asking for guidance or consulting with a doctor, can be hopelessly difficult. Computer-based sign language recognition technologies offer an alternative to mitigate the communication barrier faced by the deaf and hard of hearing. Despite much effort, research in this field is still in its infancy and automatic recognition of continuous signing remains a major challenge. This paper presents an ongoing research project designed to recognize continuous signing of Brazilian Sign Language (Libras) in healthcare settings. Health emergency situations and dialogues inspire the vocabulary of the signs and sentences we are using to contribute to the field301Vision-based human activity recognition8289COORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPESnão te

Interactive tools for volumetric neuroimage based diagnosis

Orientador: Wu Shin-TingDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: Por apresentar alta resolução espacial e espectral, é crescente o uso de imagens de ressonância magnética tanto no estudo dos órgãos humanos como também no diagnóstico das anormalidades estruturais e funcionais e no planejamento e treinamento cirúrgico. Junto com a rápida evolução dos algoritmos de processamento de imagens médicas, surgiram na última década aplicativos de diagnósticos assistidos por computador especializados em mamografia, angiografia e imagens da região torácica. A complexidade estrutural do cérebro e as diferenças anatômicas individuais do crânio constituem, no entanto, ainda desafios ao desenvolvimento de um sistema de diagnóstico especializado em neuro-imagens. A intervenção de especialistas é muitas vezes imprescindível na identificação e na interpretação dos achados radiológicos. Nesta dissertação, propomos o uso de três técnicas para auxiliar os especialistas da área médica na busca por achados radiológicos sutis de forma interativa. São apresentados dois objetos de interação, lente móvel e sonda volumétrica, que permitem atualizar continuamente os dados em foco enquanto são manipulados. Com isso, é possível investigar regiões cerebrais de interesse preservando o seu contexto. E, a fim de facilitar a percepção visual das variações funcionais ou estruturais sutis, propomos utilizar um editor de funções de transferência 1D para realçar ou aumentar o contraste entre os voxels adjacentes. As ferramentas foram avaliadas por um grupo de especialistas em neuro-imagens do Laboratório de Neuro-imagens da Faculdade de Ciências Médicas da UnicampAbstract: Because of its high spatial and spectral resolution, it is increasing the use of magnetic resonance images both in the study of human organs as well as in the diagnosis of structural and functional abnormalities and in the surgery planning and training. Along with the rapid evolution of medical image processing algorithms, computer-aided diagnostics systems specialized in mammography, angiography, and computed tomography and magnetic resonance of the thorax have emerged in the last decade. The structural complexity of the brain and individual anatomical shape of skulls are, however, challenges in developing a diagnostic system specializing in neuro-imaging. Expert interventions are still essential both in the identification and in the interpretation of radiological findings. In this dissertation, we propose the use of three techniques to aid the medical experts in the search of subtle findings in an interactive way. We present two widgets, movable lens and volumetric probe, that allow one to update continuously the volume data in focus while are manipulated. In this way, it is possible to investigate brain regions of interest preserving its context. And, in order to facilitate the visual perception of the subtle functional or structural changes, we propose to use an editor of 1D transfer function to enhance or to increase the contrast between adjacent voxels. The tools were assessed by the neuro-imaging experts of the Laboratory of Neuro-Images of the Faculty of Medical Sciences of UnicampMestradoEngenharia de ComputaçãoMestre em Engenharia Elétric

Recognition of the Mental Workloads of Pilots in the Cockpit Using EEG Signals

The commercial flightdeck is a naturally multi-tasking work environment, one in which interruptions are frequent come in various forms, contributing in many cases to aviation incident reports. Automatic characterization of pilots' workloads is essential to preventing these kind of incidents. In addition, minimizing the physiological sensor network as much as possible remains both a challenge and a requirement. Electroencephalogram (EEG) signals have shown high correlations with specific cognitive and mental states, such as workload. However, there is not enough evidence in the literature to validate how well models generalize in cases of new subjects performing tasks with workloads similar to the ones included during the model's training. In this paper, we propose a convolutional neural network to classify EEG features across different mental workloads in a continuous performance task test that partly measures working memory and working memory capacity. Our model is valid at the general population level and it is able to transfer task learning to pilot mental workload recognition in a simulated operational environment

Dataset to predict mental workload based on physiological data

A high mental workload reduces the human performance and affects his/her ability to achieve a task. Despite the recent advances in neuroscience, yet there is a lack of knowledge about the interrelation between the mental processes in the brain and the produced mental workload at a giving time. The use of neuro-physiological data to assess abnormal mental states in the last decade has led to new manners to explore the brain and its association with mental workload. We present an open dataset for mental workload investigation. The dataset contains neuro-physiological recordings collected using an electroencephalogram (EEG) and an electrocardiogram (ECG). Participants were submitted to different tasks under different conditions to induce different levels of workload. In particular, three subsets were collected. First, playing the N-back test game to enforce the short term memory. Second, playing the Heat-the-Chair game (a serious game of own design) which enforce the performing of simultaneous tasks. Third, flying in an immersive simulated Airbus320 cockpit environment. The pilot must solve diverse critical situations, such as an engine failure, a sudden wind shear, or an urgent call of the air traffic controller (ATC). The design of the datasets has been validated by correlating the performance of subjects to their self-perceived difficulty. To make the dataset useful for testing the experiments, the ground-truth of mental workload of each task, both the objective and the subjective self-perceived is provided