ETD system

Electronic theses and dissertations repository


Tesi etd-04102015-154304

Thesis type
Tesi di dottorato di ricerca
HIDE: User centred Domotic evolution toward Ambient Intelligence
Settore scientifico disciplinare
Corso di studi
tutor Dott. Miori, Vittorio
tutor Prof. Giordano, Stefano
Parole chiave
  • ambient intelligence
  • interoperability
  • domotics
  • internet of things
  • machine learning
  • semantics
  • e-health
Data inizio appello
Riassunto analitico
Pervasive Computing and Ambient Intelligence (AmI) visions are still far from being achieved, especially with regard to Domotics and home applications. According to the vision of Ambient Intelligence (AmI), the most advanced technologies are those that disappear: at maturity, computer technology should become invisible. All the objects surrounding us must possess sufficient computing capacity to interact with users, the surroundings and each other. The entire physical environment in which users are immersed should thus be a hidden computer system equipped with the appropriate software in order to exhibit intelligent behavior. Even though many implementations have started to
appear in several contexts, few applications have been made available for the home environment and the general public. This is mainly due to the segmentation of standards and proprietary solutions, which are currently confusing the market with a sparse offer of uninteroperable devices and systems. Although modern houses are equipped with smart technological appliances, still very few of these appliances can be seamlessly connected to each other.
The objective of this research work is to take steps in these directions by proposing, on the one hand, a software system designed to make today’s heterogeneous, mostly incompatible domotic systems fully interoperable and, on the other hand, a feasible software application able to learn the behavior and habits of home inhabitants in order to actively contribute to anticipating user needs, and preventing emergency situations for his health. By applying machine learning techniques, the system offers a complete, ready-to-use practical application that learns through interaction with the user in order to improve life quality in a technological living environment, such as a house, a smart city and so on.
The proposed solution, besides making life more comfortable for users without particular needs, represents an opportunity to provide greater autonomy and safety to disabled and elderly occupants, especially the critically ill ones.
The prototype has been developed and is currently running at the Pisa CNR laboratory, where a home environment has been faithfully recreated.