Recurrent Neural Networks: Design and Applications
Recurrent Neural Networks: Design and Applications
Dynamic Daily-Living Patterns and Association Analyses in Tele-Care Systems
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Smart Environments: Technology, Protocols and Applications (Wiley Series on Parallel and Distributed Computing)
Creating an Ambient-Intelligence Environment Using Embedded Agents
IEEE Intelligent Systems
Activity Recognition and Abnormality Detection with the Switching Hidden Semi-Markov Model
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
IEEE Pervasive Computing
Everyday Life Discoveries: Mining and Visualizing Activity Patterns in Social Science Diary Data
IV '07 Proceedings of the 11th International Conference Information Visualization
Proactive Fuzzy Control and Adaptation Methods for Smart Homes
IEEE Intelligent Systems
Modelling of Behavioural Patterns for Abnormality Detection in the Context of Lifestyle Reassurance
CIARP '08 Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications
Review: The use of pervasive sensing for behaviour profiling - a survey
Pervasive and Mobile Computing
Chaotic model with data assimilation using NARX network
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Prediction of chaotic time series based on the recurrent predictor neural network
IEEE Transactions on Signal Processing
The role of prediction algorithms in the MavHome smart home architecture
IEEE Wireless Communications
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
IEEE Transactions on Neural Networks
Hi-index | 0.00 |
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.