Volume 3 - Issue 3
Behavioural Patterns Analysis of Low Entropy People Using Proximity Data
- Muhammad Awais Azamy
School of Engineering and Information Sciences Middlesex University, London, UK
m.azam@mdx.ac.uk
- Jonathan Loo
School of Engineering and Information Sciences Middlesex University, London, UK
j.loo@mdx.ac.uk
- Sardar Kashif Ashraf Khan
School of Engineering and Information Sciences Middlesex University, London, UK
s.khan@mdx.ac.uk
- Usman Naeem
School of Architecture, Computing and Engineering University of East London, UK
u.naeem@uel.ac.uk
- Muhammad Adeel
School of Electronics Engineering and Computer Sciences Queen Mary University of London, UK
muhammad.adeel@qmul.ac.uk
- Waleed Ejaz
Department of Information and Communication Engineering Sejong University, Seoul, Republic of Korea
waleed.ejaz@yahoo.com
Keywords: Human Behaviour, Contextual Information, Low Entropy Behaviour, Repeated Activity Patterns.
Abstract
Over the years, wireless enabled mobile devices have become an important part of our daily activities
that can provide rich contextual information about the location and environment of an individual (for
example who is in your proximity? and where are you?). Advancement in technology has opened
several horizons to analyse and model this contextual information for human behaviour understanding.
Objective of this research work is to utilise this information from wireless proximity data to
find repeated patterns in daily life activities and individual behaviours. These repeated patterns can
give information about the unusual activities and behaviour of an individual. To validate and further
investigate this concept, we used Bluetooth proximity data in this paper. Repeated activity patterns
and behaviour of low entropy mobile people are detected by using two different techniques, N-gram
and correlative matrix techniques. Primary purpose was to find out whether contextual information
obtained from Bluetooth proximity data is useful for activities and behaviour detection of individuals.
Results have shown that these repeated patterns not only show short term daily routines but can
also show the long term routines such as, monthly or yearly patterns in an individual’s daily life that
can further help to analyse more complex and abnormal routines of human behaviour.