Poster presentation of RoutineMaker application concept at the PerCom 2012 (IEEE International conference on Pervasive Computing and Communications)

1. VTT TECHNICAL RESEARCH CENTRE OF FINLAND
www.vtt.fi
RoutineMaker
Towards End-User Automation of Daily Routines Using Smartphones
Ville Antila, Jussi Polet, Arttu Lämsä, Jussi Liikka
Context-Awareness and Service Interaction
VTT Technical Research Centre of Finland
Oulu, Finland
{ville.antila, jussi.polet, arttu.lamsa, jussi.liikka}@vtt.fi
Smartphones are becoming ubiquitous and ever more
important for the daily activities of their users. The
multitude of smartphone applications are used almost
everywhere at any time, so that some of them have
become daily routines.
Examples of routine-like behaviour can include
checking e-mail in the morning, reading the news
or listening to music while commuting, navigating or
checking-in to places to assess and comment our on-
the-go experiences. People also use smartphones to
complement other daily activities or routines, such as
watching TV or going to the grocery store.
What we did
• We developed an application to detect the day-to-
day smartphone use by logging the applications’
usage and locations.
• We developed an algorithm to process and
analyse the logged usage data into identifiable
patterns.
• We developed a smartphone application with a
functionality to create automated “tasks” out of
the identified patterns.
• We conducted a two-week user study to analyse
the approach and to receive user feedback.
Prototype system
Routine detection The prototype consists of:
The algorithm is split into two main phases, • A mobile application (for Android 2.2 and onwards
geographical and application clustering: devices), which collects usage data (locations and
• Geographical clustering discovers the most applications used), sends it to the server and presents
significant locations from the data (visited or the processed usage data to the user, visualized as
stayed most often). locations on a map. If the user notices helpful or
useful routines from the data, an automated routine
• After the geographical clustering is done, an can be created out of it.
application matrix is generated inside each
geographical cluster and filtered time wise in • A back-end service, which performs the data storage,
order to get the applications’ usage times. processing and provides the processed data for the
client(s).