Blood type classification using computer vision and machine learning

In emergency situations, where time for blood transfusion is reduced, the O negative blood type (the universal donor) is administrated. However, sometimes even the universal donor can cause transfusion reactions that can be fatal to the patient. As commercial systems do not allow fast results and are not suitable for emergency situations, this paper presents the steps considered for the development and validation of a prototype, able to determine blood type compatibilities, even in emergency situations. Thus it is possible, using the developed system, to administer a compatible blood type, since the first blood unit transfused. In order to increase the system’s reliability, this prototype uses different approaches to classify blood types, the first of which is based on Decision Trees and the second one based on support vector machines. The features used to evaluate these classifiers are the standard deviation values, histogram, Histogram of Oriented Gradients and fast Fourier transform, computed on different regions of interest. The main characteristics of the presented prototype are small size, lightweight, easy transportation, ease of use, fast results, high reliability and low cost. These features are perfectly suited for emergency scenarios, where the prototype is expected to be used.

Towards Emotional Interaction: Using Movies to Automatically Learn Users' Emotional States

The HCI community is actively seeking novel methodologies to gain insight into the user’s experience during interaction with both the application and the content. We propose an emotional recognition engine capable of automatically recognizing a set of human emotional states using psychophysiological measures of the autonomous nervous system, including galvanic skin response, respiration, and heart rate. A novel pattern recognition system, based on discriminant analysis and support vector machine classifiers is trained using movies’ scenes selected to induce emotions ranging from the positive to the negative valence dimension, including happiness, anger, disgust, sadness, and fear. In this paper we introduce an emotion recognition system and evaluate its accuracy by presenting the results of an experiment conducted with three physiologic sensors.

Dashboard services for pragmatics-based interoperability in cloud and ubiquitous manufacturing

The real Cloud and Ubiquitous Manufacturing systems require effectiveness and permanent availability of resources, their capacity and scalability. One of the most important problems for applications management over cloud based platforms, which are expected to support efficient scalability and resources coordination following SaaS implementation model, is their interoperability. Even application dashboards need to easily incorporate those new applications, their interoperability still remains a big problem to override. So, the possibility to expand these dashboards with efficiently integrated communicational cloud based services (cloudlets) represents a relevant added value as well as contributes to solving the interoperability problem. Following the architecture for integration of enriched existing cloud services, as instances of manufacturing resources, this paper: a) proposes a cloud based web platform to support dashboard integrating communicational services, and b) describe an experimentation to sustain the theory that the effective and efficient interoperability, especially in dynamic environments, could be achieved only with human intervention.

DESENVOLVIMENTO DE UM CLASSIFICADOR PARA ANÁLISE BIOMÉTRICA DE UM COLETE INSTRUMENTADO PARA REEDUCAÇÃO POSTURAL

Ao longo dos tempos tem existido um avanço, nas empresas, dirigido à preocupação com o bemestar dos trabalhadores, adotando por isso medidas preventivas. A formação especializada em Medicina do Trabalho é indispensável para o exercício de atividades de prevenção dos riscos profissionais e de promoção da saúde. A postura corporal pode ser definida como a posição e a orientação global do corpo e membros relativamente uns aos outros. Qualquer desvio na forma da coluna vertebral pode gerar solicitações funcionais prejudiciais que ocasionam um aumento de fadiga no trabalhador e leva ao longo do tempo a lesões graves. Cada vez mais surgem doenças profissionais provocadas pela adoção de más posturas, na realização de tarefas diárias dos trabalhadores. A boa postura corporal é uma tarefa específica que representa uma interação complexa entre a função biomecânica e neuromuscular. No presente plano de dissertação foram estudados diferentes classificadores tendo como objetivo classificar boas e más posturas corporais de trabalhadores em contexto de trabalho. Assim foram estudados diferentes classificadores de machine learnig, redes neuronais artificiais, support vector machine, árvores de decisão, análise discriminante, regressão logística, treebagger e naíve bayes. Para treino de classificadores foi realizada a aquisição tridimensional da postura da espinha a 100 pessoas, passando por uma parametrização e treino de diferentes classificadores para a determinação automática do tipo de postura corporal. O classificador que obteve melhor desempenho foi o Treebagger com uma classificação para True Positive de 93,3% e True Negative de 96,2%.