O. , , pp.2008-2010

, Ambient Assisted Living Test Area (AALTA)

, Figure 2.2.3: Picture of the kitchen (before equipping sensors and machines)

, Ambient Assisted Living Test Area (AALTA), Figure 2.2.4: Picture of the sleeping zone. 2.2

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, Une des activités doit pouvoir être réalisée dans une pièce de l'appartement équipé dans laquelle aucune autre activité ne peut être réalisée. Ainsi, les événements observés dans cette zone seront systématiquement liés à cette activité uniquement. Dans notre cas d'application, l'activité A 3 répond à ce critère

, Une activité doit avoir deux actions sémantiquement proches, mais en pratique, réalisée différemment. Les actions Préparer un plat préparé et Faire des pâtes de notre cas d'étude répond à ce critère

, Un des moyens pour réaliser une activité doit être si proche d'une autre activité que la distinction entre ces deux activités est difficile. Dans notre cas, les actions préparées des pâtes de l'activité A 1 et préparer du thé de l'activité A 2 sont suffisamment proches pour répondre à ce cas

, En contraste avec le cas précédent, deux actions d'activités différentes doivent avoir une petite partie de leurs réalisations en commun et une grande partie différente

, Découverte d'activité

, La première contribution de cette thèse est le développement d'une nouvelle méthode de découverte d'activité (AD) Cette approche est nécessaire, car la quatrième limitation rejetant l'étiquetage des données d'apprentissage présentées précédemment est incompatible

, Le principal avantage de la méthode développée est sa portabilité. En effet, elle est applicable dans tous les appartements équipés, quelleque soit la pathologie de l'habitant

, état probabiliste (PFA) La perte d'information liée au rejet du savoir des activités réalisées pendant la période d'apprentissage est compensée par l'ajout d'un savoir expert spécifique donnant la décomposition hiérarchique des activités en actions puis en événements capteurs

, Le modèle de chaque activité est généré en trois étapes: 1. La structure du modèle est automatiquement créée à partir de la décomposition experte

, La base de données d'apprentissage est analysée en faisant glisser une fenêtre d'observation composée d'un nombre fixe d'événements et des indicateurs de fréquences pertinentes sont calculés

, Les probabilités de nos modèles sont calculées en utilisant les indicateurs de fréquence calculés à l

, Les modèles générés par cette découverte d'activités sont ensuite utilisés comment entrée pour la reconnaissance d'activité. BIBLIOGRAPHY L'utilisation des modèles générés et des activités reconnues peut être envisagée afin de détecter de potentielles déviations d'habitudes de l'habitant pouvant être symptomatique de certaines pathologies. Enfin, il peut être envisagé d'identifier automatiquement des activités non listées comme "à surveiller

, En effet, l'ajout automatique de ce genre d'activité non sensible peut faciliter la reconnaissance en évitant de potentiels faux positifs