Behavioural pattern identification and prediction in intelligent environments

Tools

Mahmoud, S., Lotfi, A. ORCID: 0000-0002-5139-6565 and Langensiepen, C., 2013. Behavioural pattern identification and prediction in intelligent environments. Applied Soft Computing, 13 (4), pp. 1813-1822. ISSN 1568-4946

Preview

Text
212075_Applied_Soft_Computing_2013.pdf
Download (2MB) | Preview

Official URL: http://dx.doi.org/10.1016/j.asoc.2012.12.012

Abstract

In this paper, the application of soft computing techniques in prediction of an occupant's behaviour in an inhabited intelligent environment is addressed. In this research, daily activities of elderly people who live in their own homes suffering from dementia are studied. Occupancy sensors are used to extract the movement patterns of the occupant. The occupancy data is then converted into temporal sequences of activities which are eventually used to predict the occupant behaviour. To build the prediction model, different dynamic recurrent neural networks are investigated. Recurrent neural networks have shown a great ability in finding the temporal relationships of input patterns. The experimental results show that non-linear autoregressive network with exogenous inputs model correctly extracts the long term prediction patterns of the occupant and outperformed the Elman network. The results presented here are validated using data generated from a simulator and real environments.

Item Type:

Journal article

Publication Title:

Applied Soft Computing

Creators:

Mahmoud, S., Lotfi, A. and Langensiepen, C.

Publisher:

Elsevier

Date:

2013

Volume:

Number: