Abstract
Ambient Assisted Living is a promising research area.It aims to use information technologies to assist dependent elderly people on their daily life.The impact of these technologies could be dramatic for millions of elderly people and for their caregivers.This research area has developed consistently over the past few years, although slower than most other applications of the Internet of Things.This is caused by the inner complexity of Ambient Assisted Living.Indeed, Ambient Assisted Living requires a dynamic understanding of the context, as well as the disposal of numerous communication media in the environment surrounding the end-user.More precisely, researchers face difficulties in recognizing end-users' activities, as we can observe in the literature.My research team have deployed our system in several environments, of which the most recent includes a nursing home and three houses in France.We adopt a user-centric approach, where end-users describe what they expect, and share with us their feedbacks and advices about our system.This approach guided me to identify activity recognition as a critical challenge that needs to be addressed for the usability and acceptability of Ambient Assisted Living solutions.Thus, the guiding line of this thesis work emerges naturally from the challenges we encountered during our deployments.In the beginning of this thesis, I have been facing the practical problem of putting into place an actual deployment of our system.In this document, I describe the needs that emerged from our own observations and from the users feedbacks, as well from as the technical problems we encountered.For each of these problems and needs, I describe the solution we have selected and implemented.From our deployments, my team and I were able to collect a large amount of operating data.I have created a platform to analyze Ambient Assisted Living data, also to allow rapid prototyping for activity recognition.By using this platform, I have observed problems with activity recognition, which is too often misleading and inaccurate.A first observation is that the sensor events are sometimes disturbed by multiuser situations, when several persons are active in the home.Activity recognition in these conditions is extremely difficult, and during this thesis my scope is solely focused on detecting multiuser situations, not recognizing activities in such situations.I then seek to improve the quality of our reasoning engine.To do so, I have looked more precisely at some incorrect reasoning.I observed that the errors in reasoning come from the fact that the reasoner tries to be too precise or that, conversely, it infers too imprecise activities.I therefore propose a method to optimize the reasoning engine, so that it concludes with the best possible activity among several possible activities, by choosing the one that offers the best compromise between Precision and the risk of Inaccuracy in activity recognition.It should be noted that this contribution is independent of the method used for activity recognition, and can work with any type of reasoning.I have formalized the concept of Accuracy, and provided a method to measure the Accuracy of a reasoning engine.This requires first to observe a ground-truth on the activity being performed.This contribution brought me to introduce a hierarchical model for activities.Indeed, by applying the method described above on a hierarchical model of activities, the reasoning engine can be calibrated automatically to choose how precise it should be at recognizing an activity.It goes without saying that these contributions are formally validated through this dissertation.