The dissertation presents a model to express the most relevant features of the human behaviour, specifying two personalized instances for retail and health monitoring domains. These features are divided into static and dynamic. For the static ones facial features are the most relevant since they can express different attributes of the subjects such as age, gender or emotions. For that purpose landmarks and pose are the most significant points of interest. However, the body pose can also reveal some interesting features of the current state of one person and should also be taken into account. Regarding the dynamic properties, the trajectories described by a subject in the monitored scenario can also express very useful insights. It is a basic task to delimit the area where the subject is located, that is mandatory to perform the static inferences. The interactions of the user with the environment are a very relevant feature, and should be modeled as well.
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