Learning frequent behaviours of the users in intelligent environments

Aztiria, Asier, Augusto, Juan Carlos ORCID: https://orcid.org/0000-0002-0321-9150, Basagoiti, Rosa, Izaguirre, Alberto and Cook, Diane J. (2013) Learning frequent behaviours of the users in intelligent environments. IEEE Transactions on Systems Man and Cybernetics: Systems, 43 (6) . pp. 1265-1278. ISSN 2168-2216 [Article] (doi:10.1109/TSMC.2013.2252892)

Abstract

Intelligent environments (IEs) are expected to support people in their daily lives. One of the hidden assumptions in IEs is that they propose a change of perspective in the relationships between humans and technology, shifting from a techno-centered perspective to a human-centered one. Unlike current computing systems where the user has to learn how to use the technology, an IE adapts its behavior to the users, even anticipating their needs, preferences, or habits. For this reason, the environment should learn how to react to the actions and needs of the users, and this should be achieved in an unobtrusive and transparent way. In order to provide personalized and adapted services, it is necessary to know the preferences and habits of users. Thus, the ability to learn patterns of behavior becomes an essential aspect for the successful implementation of IEs. This paper presents a system, learning frequent patterns of user behavior system (LFPUBS), that discovers users' frequent behaviors taking into consideration the specific features of IEs. The core of LFPUBS is the learning layer, which, unlike some other components, is independent of the particular environment in which the system is being applied. On one hand, it includes a language that allows the representation of discovered behaviors in a clear and unambiguous way. On the other hand, coupled with the language, an algorithm that discovers frequent behaviors has been designed and implemented. For this reason, it uses association, workflow mining, clustering, and classification techniques. LFPUBS was validated using data collected from two real environments. In MavPad environment, LFPUBS was tested with different confidence levels using data collected in three different trials, whereas in a WSU Smart Apartment environment LFPUBS was able to discover a predefined behavior.

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