An intervention mechanism for assistive living in smart homes

Authors:
Shuai Zhang;Sally Mcclean;Bryan Scotney;Xin Hong;Chris Nugent;Maurice Mulvenna
Affiliations:
(Correspd. E-mail: s.zhang@ulster.ac.uk) School of Computing and Information Engineering, University of Ulster, Coleraine campus, Cromore Road, Coleraine, Co. Londonderry, BT52 1SA, Northern Irela ...;School of Computing and Information Engineering, University of Ulster, Coleraine campus, Cromore Road, Coleraine, Co. Londonderry, BT52 1SA, Northern Ireland;School of Computing and Information Engineering, University of Ulster, Coleraine campus, Cromore Road, Coleraine, Co. Londonderry, BT52 1SA, Northern Ireland;School of Computing and Mathematics, University of Ulster, Jordanstown campus, Shore Road, Newtownabbey, Co. Antrim, BT37 0QB, Northern Ireland;School of Computing and Mathematics, University of Ulster, Jordanstown campus, Shore Road, Newtownabbey, Co. Antrim, BT37 0QB, Northern Ireland;School of Computing and Mathematics, University of Ulster, Jordanstown campus, Shore Road, Newtownabbey, Co. Antrim, BT37 0QB, Northern Ireland
Venue:
Journal of Ambient Intelligence and Smart Environments
Year:
2010

Citing 14
Cited 1

Signal detection theory: valuable tools for evaluating inductive learning

Proceedings of the sixth international workshop on Machine learning
Robust classification systems for imprecise environments

AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Mining Sequential Patterns

ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Location Aware Resource Management in Smart Homes

PERCOM '03 Proceedings of the First IEEE International Conference on Pervasive Computing and Communications
Inside the Smart House

Inside the Smart House
Inferring Activities from Interactions with Objects

IEEE Pervasive Computing
Fine-Grained Activity Recognition by Aggregating Abstract Object Usage

ISWC '05 Proceedings of the Ninth IEEE International Symposium on Wearable Computers
Discovery of interesting episodes in sequence data

Proceedings of the 2006 ACM symposium on Applied computing
Online Sequential Prediction via Incremental Parsing: The Active LeZi Algorithm

IEEE Intelligent Systems
Decision Support for Alzheimer's Patients in Smart Homes

CBMS '08 Proceedings of the 2008 21st IEEE International Symposium on Computer-Based Medical Systems
Where is . •.? learning and utilizing motion patterns of persons with mobile robots

IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
The use of the area under the ROC curve in the evaluation of machine learning algorithms

Pattern Recognition
homeML: an open standard for the exchange of data within smart environments

ICOST'07 Proceedings of the 5th international conference on Smart homes and health telematics
A planning system based on Markov decision processes to guide people with dementia through activities of daily living

IEEE Transactions on Information Technology in Biomedicine

Capacitive indoor positioning and contact sensing for activity recognition in smart homes

Journal of Ambient Intelligence and Smart Environments

Quantified Score

Hi-index	0.00

Visualization

Abstract

In order to support ageing in place for elderly people, technologies and services for home environments need to be developed. An intervention mechanism is proposed in this paper in a smart home environment to provide reminders to assist elderly inhabitants to complete activities of daily living (ADL). The situation of multiple inhabitants in a single smart environment is addressed. A probabilistic learning approach is proposed to characterise inhabitants' behavioural patterns, learned from summary activities collected during a period. Activity reasoning can then be carried out given partially observed low-level sensor information. Decision support is used to monitor inhabitants' activities and thus to assist the completion of tasks if necessary. Personalised reminders at various levels of detail can be delivered based on individual need and preference. Appropriate thresholds are learned to be used to ensure delivery of predictions for which confidence is high, to avoid confusing inhabitants with incorrect reminders. The potential of our approach to support assistive living and home-health monitoring of elder patients is demonstrated.