Les travaux décrits dans ce document abordent le problème du couplage dynamique homme-système haptique. Nous proposons une étude de ce couplage basée sur l'hypothèse d'un système hybride temporaire. Selon cette hypothèse, le système formé lors du couplage peut être considéré comme un système dynamique dont les deux parties ne peuvent pas être séparées. Ce sujet est pluridisciplinaire, se situant à l'intersection des sciences cognitives, de l'automatique et de l'haptique. La première partie du document comporte un état de l'art sur l'analyse du couplage dans ces trois domaines, une description de la problématique et de la méthode à utiliser pour notre étude ainsi qu'une proposition des typologies du geste. Lors de cette étude du couplage, nous nous intéressons à un groupe de gestes particuliers, notamment le geste périodique et le geste passif dans une situation de simulation haptique ainsi qu'aux modèles d'interaction capables de les générer. La méthode générale, consiste à définir des approches pour la modélisation du couplage main-système haptique pour ensuite réaliser une analyse du système couplé à partir d'une acquisition des données du système lors du couplage et en utilisant des méthodes d'identification de paramètres issus de l'automatique pour caractériser les modèles. La dernière partie, décrit la mise en place du dispositif pour l'analyse expérimentale du couplage en situation de simulation avec une interaction haptique. Ce dispositif permet l'acquisition des données du geste pour l'analyse. Nous présentons également, l'étude réalisée sur le simulateur haptique afin d'établir l'équivalence entre les paramètres virtuels introduits et issus du simulateur et des paramètres physiques réels. Ensuite, nous décrivons l'analyse expérimentale des différentes situations de couplage proposées. Les expériences effectuées lors de cette étude ont été réalisées sur la plateforme temps réel ERGON_X, conçue par l'ACROE/ICA. Les résultats de ces expériences ont permis de quantifier les modèles du geste et d'observer ses composantes, selon les modèles établis. Mots clés : haptique, interface haptique, interfaces homme-machine, simulation temps réel, couplage homme-objet, geste, modélisation physique, identification de paramètres. / The work described in this document deals with the problem of human-haptic system dynamic coupling. We propose a study of this kind of coupling based on the hypothesis of a temporary hybrid system. Under this hypothesis, the system formed during the coupling can be considered as a dynamic system in which the two parties that compose it cannot be separated. This is multidisciplinary topic, situated at the intersection of cognitive science, automation and haptics. The first part of the document includes a state of the art on the analysis of coupling in these three areas, the description of the problem and the methodology for the study as well as a proposal of gesture typology. In this study of coupling, we are interested in a particular group of actions, such as periodic movement and passive gesture in a situation of haptic simulation and also, in the interaction models able to generate them. The general method is to define the approaches for modeling the hand-haptic device coupling and then perform an analysis of the coupled system by acquiring system data during the coupling and using parameter identification methods to characterize the models. The final section describes the implementation of the device for the experimental analysis of coupling during simulation with a haptic interaction. This device allows data acquisition for gesture analysis. We also present the study of the haptic simulator to establish the equivalence between virtual parameters introduced to and returned by the simulator and real physical parameters. Then, we describe the experimental analysis of different proposed coupling situations. The experiments performed for this study were performed using the real-time platform ERGON_X, designed by ACROE / ICA. The results of these experiments were used to quantify gesture models and to observe its components, according to established models. Keywords: haptic, haptic interface, human-machine interfaces, real-time simulation, human-object coupling, gesture, physical modeling, parameter identification.

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Recent advances in camera technology have made it possible to build a comfortable, wearable system which can capture the scene in front of the user throughout the day. Products based on this technology, such as GoPro and Google Glass, have generated substantial interest. In this thesis, I present my work on egocentric vision, which leverages wearable camera technology and provides a new line of attack on classical computer vision problems such as object categorization and activity recognition. The dominant paradigm for object and activity recognition over the last decade has been based on using the web. In this paradigm, in order to learn a model for an object category like coffee jar, various images of that object type are fetched from the web (e.g. through Google image search), features are extracted and then classifiers are learned. This paradigm has led to great advances in the field and has produced state-of-the-art results for object recognition. However, it has two main shortcomings: a) objects on the web appear in isolation and they miss the context of daily usage; and b) web data does not represent what we see every day. In this thesis, I demonstrate that egocentric vision can address these limitations as an alternative paradigm. I will demonstrate that contextual cues and the actions of a user can be exploited in an egocentric vision system to learn models of objects under very weak supervision. In addition, I will show that measurements of a subject's gaze during object manipulation tasks can provide novel feature representations to support activity recognition. Moving beyond surface-level categorization, I will showcase a method for automatically discovering object state changes during actions, and an approach to building descriptive models of social interactions between groups of individuals. These new capabilities for egocentric video analysis will enable new applications in life logging, elder care, human-robot interaction, developmental screening, augmented reality and social media.