Capturing Regular Human Activity through a Learning Context Memory

A learning context memory consisting of two main parts is

presented. The first part performs lossy data compression,

keeping the amount of stored data at a minimum by combining

similar context attributes — the compression rate for the

presented GPS data is 150:1 on average. The resulting data is

stored in an appropriate data structure highlighting the level

of compression. Elements with a high level of compression

are used in the second part to form the start and end points

of episodes capturing common activity consisting of consecutive

events. The context memory is used to investigate how

little context data can be stored containing still enough information

to capture regular human activity.