Using wireless proximity data to infer the behaviour of mobile people

Abstract

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 compares the detected tasks with the plans and recognises the high level activities that the user is performing.

Findings

The paper provides an insight to how only wireless proximity data can be utilised to recognise high level activities and behaviour of individuals. A number of scenarios and experiments are designed to prove the validity of the proposed methodology.

Research limitations/implications

This paper focussed on relatively low entropy individuals with regular routines and behavioural patterns which can be improved by increasing the level of entropies in behavioural routines.

Practical implications

The paper includes implications for the utilisation in health care environments for elderly people and physically impaired individuals.

Originality/value

This paper provides a detailed and original study of algorithms and techniques that can be used to recognise high level activities and behaviour of individuals by using only wireless proximity data.

Citation

Awais Azam, M. and Loo, J. (2013), "Using wireless proximity data to infer the behaviour of mobile people", International Journal of Pervasive Computing and Communications, Vol. 9 No. 1, pp. 6-36. https://doi.org/10.1108/17427371311315734