Caracterización de gestos faciales mediante electromiografía superficial

La electromiografía (EMG) es el estudio del comportamiento de las señales eléctricas generadas por los músculos al realizar movimientos; Cuando se cumple la función de masticar y hasta sonreír, se hace uso de una gran cantidad de músculos en la cara y son estos los más importantes en la cabeza. El movimiento realizado por estos músculos garantizan que haya movimientos y gestos fáciles correctamente; para estudiar estas señales musculares es necesario realizar electromiografía facial, que se dividen en 4 grupos Epicraneales, Orbiculares de los ojos, boca y nasales, que son superficiales y están en la epidermis con movimientos verticales y anteroposteriores de la cara, esta herramienta proporciona información acerca del estado de los nervios y músculos. La caracterización de las señales electromiografícas de la cara permitiría el desarrollo de sistemas capaces de ayudar a personas con discapacidad físico-motora a realizar tareas cotidianas con mayor facilidad, pues los sistemas que existentes funcionan con controles remotos, dispositivos móviles, señales sonoras. Los dispositivos que funcionan basándose en señales electromiografícas están orientados a estudios médicos y la rehabilitación de pacientes con distrofia muscular parcial. Para lograr caracterizar estas señales se propone realizar toma de datos y crear un banco de señales para el análisis, posteriormente utilizar series de tiempo y redes neuronales para diferenciar patrones y clasificar dichas señales. Aprovechando los datos que se obtienen con el sensor DFRobot Heart Rate Monitor Sensor, utilizado para adquirir señales de electromiografía facial, se realizara una comparación y caracterización de señales, estadísticamente para encontrar patrones que permitan la caracterización de cada movimiento facial

Models and Analysis of Vocal Emissions for Biomedical Applications

The International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications (MAVEBA) came into being in 1999 from the particularly felt need of sharing know-how, objectives and results between areas that until then seemed quite distinct such as bioengineering, medicine and singing. MAVEBA deals with all aspects concerning the study of the human voice with applications ranging from the neonate to the adult and elderly. Over the years the initial issues have grown and spread also in other aspects of research such as occupational voice disorders, neurology, rehabilitation, image and video analysis. MAVEBA takes place every two years always in Firenze, Italy. This edition celebrates twenty years of uninterrupted and succesfully research in the field of voice analysis

Classificação de atividade eletromiográfia facial de indivíduos saudáveis e com hanseníase por meio de máquina de vetores de suporte

A number of studies in the areas of Biomedical Engineering and Health Sciences have employed machine learning tools to develop methods capable of identifying patterns in different sets of data. Despite its extinction in many countries of the developed world, Hansen’s disease is still a disease that affects a huge part of the population in countries such as India and Brazil. In this context, this research proposes to develop a method that makes it possible to understand in the future how Hansen’s disease affects facial muscles. By using surface electromyography, a system was adapted so as to capture the signals from the largest possible number of facial muscles. We have first looked upon the literature to learn about the way researchers around the globe have been working with diseases that affect the peripheral neural system and how electromyography has acted to contribute to the understanding of these diseases. From these data, a protocol was proposed to collect facial surface electromyographic (sEMG) signals so that these signals presented a high signal to noise ratio. After collecting the signals, we looked for a method that would enable the visualization of this information in a way to make it possible to guarantee that the method used presented satisfactory results. After identifying the method's efficiency, we tried to understand which information could be extracted from the electromyographic signal representing the collected data. Once studies demonstrating which information could contribute to a better understanding of this pathology were not to be found in literature, parameters of amplitude, frequency and entropy were extracted from the signal and a feature selection was made in order to look for the features that better distinguish a healthy individual from a pathological one. After, we tried to identify the classifier that best discriminates distinct individuals from different groups, and also the set of parameters of this classifier that would bring the best outcome. It was identified that the protocol proposed in this study and the adaptation with disposable electrodes available in market proved their effectiveness and capability of being used in different studies whose intention is to collect data from facial electromyography. The feature selection algorithm also showed that not all of the features extracted from the signal are significant for data classification, with some more relevant than others. The classifier Support Vector Machine (SVM) proved itself efficient when the adequate Kernel function was used with the muscle from which information was to be extracted. Each investigated muscle presented different results when the classifier used linear, radial and polynomial kernel functions. Even though we have focused on Hansen’s disease, the method applied here can be used to study facial electromyography in other pathologies.Dissertação (Mestrado)Muitos estudos na área de engenharia biomédica e de ciências da saúde têm buscado a área de aprendizado de máquina para desenvolver métodos que sejam capazes de identificar padrões em diferentes conjuntos de dados. Apesar de extinta em muitos países do primeiro mundo, a hanseníase ainda é uma doença que atinge uma grande parte da população de países como Índia e Brasil. Nesse contexto, essa pesquisa visa a criação de um método que possibilite futuramente entender como a hanseníase afeta os músculos da face. Utilizando a eletromiografia de superfície, um sistema foi adaptado para que se pudesse captar os sinais do maior número possível dos músculos da face desses indivíduos. Buscou-se primeiro na literatura a forma como pesquisadores ao redor do mundo estão trabalhando com doenças que afetam o sistema nervosa periférico e como a eletromiografia tem atuado para contribuir no entendimento dessas doenças. A partir dessas informações, um protocolo foi proposto para se coletar sinais eletromiográficos (sEMG) da face de forma que estes sinais apresentassem uma alta relação sinal/ruído. Depois de coletar os sinais, os pesquisadores buscaram um método que possibilitasse a visualização dessa informação de forma que fosse possível garantir que o método utilizado apresentava resultados satisfatórios. Após identificar que o método foi eficaz, os pesquisadores buscaram entender quais as informações podem ser extraídas do sinal de eletromiografia que representem os dados coletados. Como não se encontrou na literatura estudos que demonstrem quais informações podem contribuir para melhor entendimento dessa patologia, foram extraídos do sinal parâmetros de amplitude, frequência e entropia, e um algoritmo de seleção de características foi utilizado para que se buscasse as características que melhor distinguem um indivíduo saudável do patológico. Em seguida buscou-se identificar o classificador que melhor discriminou indivíduos saudáveis daqueles com hanseníase. Foi identificado que o protocolo proposto neste estudo e a adaptação feita nos eletrodos descartáveis presentes no mercado se mostraram eficientes e que podem ser utilizadas em diferentes estudos quando se deseja coletar dados da eletromiografia da face. O algoritmo de seleção de características também mostrou que nem todas as características extraídas do sinal são significantes para a classificação dos dados, sendo algumas mais relevantes do que outras. O classificador Support Vector Machine (SVM) – Máquina de Vetores de Suporte - se mostrou eficiente quando utilizada a função Kernel adequada para o músculo em que se deseja extrair as informações. Cada músculo estudado neste artigo apresentou resultados diferentes quando o classificador utilizou funções Kernel linear, radial e polinomial. Embora tenha focado em hanseníase, o método utilizado aqui pode ser aplicado para estudar eletromiografia da face em inúmeras outras patologias

Models and Analysis of Vocal Emissions for Biomedical Applications

The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies

Magnetoencephalography

This is a practical book on MEG that covers a wide range of topics. The book begins with a series of reviews on the use of MEG for clinical applications, the study of cognitive functions in various diseases, and one chapter focusing specifically on studies of memory with MEG. There are sections with chapters that describe source localization issues, the use of beamformers and dipole source methods, as well as phase-based analyses, and a step-by-step guide to using dipoles for epilepsy spike analyses. The book ends with a section describing new innovations in MEG systems, namely an on-line real-time MEG data acquisition system, novel applications for MEG research, and a proposal for a helium re-circulation system. With such breadth of topics, there will be a chapter that is of interest to every MEG researcher or clinician

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

Models and analysis of vocal emissions for biomedical applications: 5th International Workshop: December 13-15, 2007, Firenze, Italy

The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies. The Workshop has the sponsorship of: Ente Cassa Risparmio di Firenze, COST Action 2103, Biomedical Signal Processing and Control Journal (Elsevier Eds.), IEEE Biomedical Engineering Soc. Special Issues of International Journals have been, and will be, published, collecting selected papers from the conference

30th International Conference on Condition Monitoring and Diagnostic Engineering Management (COMADEM 2017)

Proceedings of COMADEM 201