Over the years, wireless enabled mobile devices have become an important part of our daily activities\ud that can provide rich contextual information about the location and environment of an individual (for\ud example who is in your proximity? and where are you?). Advancement in technology has opened\ud several horizons to analyse and model this contextual information for human behaviour understanding.\ud Objective of this research work is to utilise this information from wireless proximity data to\ud find repeated patterns in daily life activities and individual behaviours. These repeated patterns can\ud give information about the unusual activities and behaviour of an individual. To validate and further\ud investigate this concept, we used Bluetooth proximity data in this paper. Repeated activity patterns\ud 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\ud obtained from Bluetooth proximity data is useful for activities and behaviour detection of individuals.\ud Results have shown that these repeated patterns not only show short term daily routines but can\ud also show the long term routines such as, monthly or yearly patterns in an individual’s daily life that\ud can further help to analyse more complex and abnormal routines of human behaviour
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