Trend Analysis Techniques in Forecasting Human Behaviour Evolution.

Analysis of human behaviour changes is a subject of interest for many researchers. This could be obtained considering either short-term or long-term changes. The aim of this study is to find long-term changes (behaviour evolution) in Activities of Daily Living (ADL) or Activities of Daily Working (ADW) of users in an Ambient Intelligence (AmI) environment. Analysis is based on introduction of a novel Human Behaviour Momentum Indicator (HBMI). Extensive experiments are conducted to investigate the effectiveness of the studied techniques on real-world datasets collected from home and office environments. To show the effectiveness of the proposed approach, results are compared with Relative Strength Index (RSI). The results show that trends in ADL or ADW can be detected and the direction of the activity's trend are predicted. In addition, the results show that our proposed technique gives a better response to changes in data more than the other technique.