Section: New Results

Learning Routine Patterns of Activity in the Home

Participants: Julien Cumin, James Crowley

Other Partners: Fano Ramparany, Greg Lefevre (Orange Labs)

During the month of February 2017, we have collected 4 weeks of data on daily activities within the Amiqual4Home Smart Home Living lab apartment. This dataset was presented at the international Conference on Ubiquitous Computing and Ambient Intelligence, UCAmI 2017, at Bethlehem PA, in Nov 2017 and is currently available for download from the Amiqual4Home web server (http://amiqual4home.inria.fr/en/orange4home/)

The objective of this research action is to develop a scalable approach to learning routine patterns of activity in a home using situation models. Information about user actions is used to construct situation models in which key elements are semantic time, place, social role, and actions. Activities are encoded as sequences of situations. Recurrent activities are detected as sequences of activities that occur at a specific time and place each day. Recurrent activities provide routines that can be used to predict future actions and anticipate needs and services. An early demonstration has been to construct an intelligent assistant that can respond to and filter inter-personal communications.