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Digital archive of theses discussed at the University of Pisa


Thesis etd-09182016-174556

Thesis type
Tesi di dottorato di ricerca
Thesis title
Ambient intelligence in assisted living environments
Academic discipline
Course of study
tutor Prof. Chessa, Stefano
  • Activity Recognition
  • Ambient Assisted Living
  • Ambient Intelligence
  • Indoor Localization
  • Stigmergy
Graduation session start date
The paradigm of Ambient Intelligence (AmI) aims at supporting humans in achieving their everyday objectives by enriching physical environments with networks of distributed devices, such as sensors, actuators, and computational resources. AmI is not only the convergence of various technologies (i.e. sensor networks and industrial electronics) and related research fields (i.e. pervasive, distributed computing, and arti cial intelligence), but it represents a major eff ort to integrate them and to make them really useful for everyday human life. In particular, the recognition of human activities, coupled with the knowledge of the user's position in the indoor environment, represent two of the main pillars of the so-called "context-awareness". A context-aware system is aware of what sensory data mean: it is able to associate meaning to observations, and to make the best use of sensory data once their meaning has been assessed. In this context, one of the most important research and development areas is represented by assisted living environments. The general goal of Ambient Assisted Living (AAL) solutions is to apply ambient intelligence technologies to enable people with specific demands, e.g. with disabilities or elderly, to live longer in their preferred environment.
This thesis deals with two major problems that still prevent the spreading of AAL solutions in real environments: (i) the need for a common medium to transmit the sensory data and the information produced by algorithms and (ii) the unobtrusiveness of context-aware applications in terms of both placement of devices and period of observations (i.e. long-term care offering services or assistance on a daily basis over a long period of time for people who are not independent).
In order to address these challenges, this thesis contributes to the Ambient Intelligence research field, applied to assisted living environments, by means of a holistic solution composed of a beyond the state-of-the-art middleware infrastructure, providing interoperability and service abstraction, and a suite of unobtrusive applications, built on top the proposed middleware, that allows the detection of the user's context and behavioral deviations of his routine in indoor activities. The proposed solution has been thoroughly evaluated in the laboratory and in real testbeds offered by European FP7 projects, namely GiraffPlus and DOREMI, that showed its effectiveness in dealing with the requirements coming from the application of the AAL paradigm in the real world.