Using Wireless Proximity Data to Infer the Behaviour of Mobile People

Purpose – The aim of the research work presented in this paper is to investigate a mechanism that can recognise high level activities (for example, going for a walk, travelling on the bus, doing evening activity, etc.) and behaviour of low entropy people (people with regular daily life routines, e.g. elderly people with dementia, patients with regular routines) in order to help them improve their health related daily life activities by using wireless proximity data (e.g. Bluetooth, Wi‐Fi).Design/methodology/approach – The paper adopted a tiered approach to recognise activities and behaviour. Higher level activities are divided into sub‐activities and tasks. Separating the tasks from the raw wireless proximity data is achieved by designing task separator (TASE) algorithm. TASE takes wireless proximity data as an input and separates it into different tasks. These detected tasks and the high level daily activity plans that are made in a planning language Asbru, are then fed into the activity recogniser that ...

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