Reproduction de : Thèse de doctorat : Génie des procédés : Toulouse, INPT : 2006. / Titre provenant de l'écran-titre. Bibliogr. 107 réf.

Thesis (Ph. D.)--University of Washington, 2001. / Vita. Includes bibliographical references (p. 186-192).

Thesis (Ph. D.)--Carnegie Mellon University, 2003. / "August 2003." Includes bibliographical references and index.

Thesis (Ph. D.)--University of Washington, 2002. / Vita. Includes bibliographical references (leaves 51-52).

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It was first pointed out by Doob and Kakutani the connection between classical potential theory and Brownian motion. In [10] one finds that if P(t,x,A) is the probability transition function, i.e. P(t,x,A) = probability that a particle moves from the point x to the Borel subset A of a set I in time t, then the potential kernal, K(x,A), is defined as follows [...]

The healthcare systems are experiencing heavy workload and high cost caused by ageing population. The assisted monitoring systems for the elderly persons, and persons with chronic diseases, promises great potential to provide them with care and comfort at the privacy of their own homes and as a result help reduce healthcare costs. This requires a monitoring system capable of detecting daily human activities in living spaces. In this work we discuss different challenges to design such a system, present an activity data visualization tool designed to study human activities in a living space and propose a two stage, supervised statistical model for detecting the activities of daily living (ADL) from non-visual sensor data streams. A novel data segmentation is proposed for accurate prediction at the first stage. We present a novel error correction structure for the second stage to boost the accuracy by correcting the misclassification from the first stage.

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