Determining Behavioural Trends in an Ambient Intelligence Environment

Abstract

Analysing changes of the behaviour of an occupant who lives in an Ambient Intelligence (AmI) environment is addressed in this paper. Changes in Activities of Daily Living (ADL) are indicators of the social and health status of the occupant. This research therefore aims to identify trends in ADL and interpret them in a suitable form for carers. It is essential for this purpose to have access to relatively long-term monitoring data of the occupant using appropriate sensory devices. Different trend analysis techniques are investigated and compared. These techniques include; Seasonal Kendall Test (SKT), Simple Moving Mean Average (SMA), and Exponentially Weighted Moving Average (EWMA), which are used to detect trends in the time-series data representing occupancy duration in different areas of a home environment for an elderly person living independently.