O objetivo deste trabalho é criar uma metodologia capaz de reconhecer gestos de mãos, a partir de imagens dinâmicas, para interagir com sistemas. Após a captação da imagem, a segmentação ocorre nos pixels pertencentes às mãos que são separados do fundo pela segmentação pela subtração do fundo e filtragem de cor de pele. O algoritmo de reconhecimento é baseado somente em contornos, possibilitando velocidade para se trabalhar em tempo real. A maior área da imagem segmentada é considerada como região da mão. As regiões detectadas são analisadas para determinar a posição e a orientação da mão. A posição e outros atributos das mãos são rastreados quadro a quadro para distinguir um movimento da mão em relação ao fundo e de outros objetos em movimento, e para extrair a informação do movimento para o reconhecimento de gestos. Baseado na posição coletada, movimento e indícios de postura são calculados para reconhecimento um gesto significativo. / The purpose of this paper is to develop a methodology able to recognize hand gestures from dynamic images to interact with systems. After the image capture segmentation takes place where pixels belonging to the hands are separated from the background based on skin-color segmentation and background extraction. The image preprocessing can be applied before the edge detection. The recognition algorithm uses edges only; therefore it is quick enough for real time. The largest blob from the segmented image will be considered as the hand region. The detected regions are analyzed to determine position and orientation of the hand for each frame. The position and other attributes of the hands are tracked per frame to distinguish a movement from the hand in relation to the background and from other objects in movement, and to extract the information of the movement for the recognition of dynamic gestures. Based in the collected position, movement and indications of position are calculated to recognize a significant gesture.

L'arrêt cardiaque est principalement d'étiologie cardio-vasculaire. Dans le contexte actuel des arrêts cardiaques extrahospitaliers, 20 à 25% des victimes présentent une fibrillation ventriculaire. Environ 3 à 5% des personnes sont sauvées sans séquelle neurologique. La survie à un arrêt cardiaque extrahospitalier dépend d'une prise en charge précoce et rapide de la victime. Les premiers témoins actifs réalisant la réanimation cardio-pulmonaire combinée à l'utilisation d'un défibrillateur sont ainsi un maillon important pour sauver la victime.Notre objectif principal est d'améliorer le taux de survie à un arrêt cardiaque extrahospitalier. Une première voie d'investigation est de proposer un fonctionnement de défibrillateur optimal combinant judicieusement les différents modules de détection embarqués (détection de fibrillation ventriculaire, détection de massage cardiaque, détection d'interférences électromagnétiques) afin de réduire les temps de pause péri-choc durant la procédure de réanimation. En effet, pendant ces temps, dits « hands-off » en anglais, aucun geste de secours n'est administré au patient qui, lui, voit d'une part sa pression de perfusion coronarienne chuter, d'autre part la probabilité de succès des tentatives de défibrillation décroître. C'est pourquoi une deuxième voie d'investigation porte sur la prédiction de l'efficacité de choc. Dans ce contexte, nous proposons de combiner des paramètres de l'électrocardiogramme dans les domaines temporel, fréquentiel et de la dynamique non-linéaire. Un classifieur bayésien utilisant le modèle de mélange de gaussiennes a été appliqué aux vecteurs de paramètres les plus prédicteurs de l'issue de la défibrillation et l'algorithme Espérance-Maximisation a permis de mener à bien la procédure d'apprentissage des paramètres du modèle probabiliste représentant les distributions conditionnelles de classe.L'ensemble des méthodes proposées a permis d'atteindre des résultats prometteurs pour à la fois réduire les temps de pause péri-choc et prédire l'efficacité de défibrillation et ainsi espérer améliorer le taux de survie à un arrêt cardiaque / The cardiac arrest is mainly of cardiovascular etiology. In the actual context of out-of-hospital cardiac arrests, 20 to 25% of the victims present a ventricular fibrillation. About 3 to 5% of the victims are saved without neurological damage. The chance of surviving a cardiac arrest outside an hospital depends on the early and fast support of the victim. The first active witnesses performing cardiopulmonary resuscitation combined with the use of a defibrillator are an important link to save the victim.Our main objective is to improve survival rate in out-of-hospital cardiac arrest cases. A first way of investigation is to propose an optimal functioning of defibrillator combining wisely the different processes of detection embedded (ventricular fibrillation detection, chest compressions detection, electromagnetic interferences detection), in order to reduce the peri-shock pauses during the resuscitation procedure. In fact, during these pauses, known as "hands-off" pauses, no emergency action is provided to the patient, what is correlated to a drop of the coronary pression, but also to a decrease of the chance of successful defibrillation. That is the reason why, a second way of investigation is based on the prediction of the efficiency of defibrillation. In this context, we propose to use a combination of parameters extracted from electrocardiogram in time, frequency and non-linear dynamics domains. A bayesian classifier using a gaussian mixture model was applied to the vectors of parameters, which are the most predictor of the defibrillation outcome and the algorithm Expectation-Maximization allowed to learn the parameters of the probabilistic model representing the class conditional distributions.All of the proposed methods allowed to reach promising results for both reducing the peri-shock pauses and predicting the efficiency of defibrillation in hope to improve the survival rate in cardiac arrest cases