Interoperable services based on activity monitoring in ambient assisted living environments

Acampora, G ORCID: https://orcid.org/0000-0003-4082-5616, Appiah, K ORCID: https://orcid.org/0000-0002-9480-0679, Hunter, A and Vitiello, A, 2014. Interoperable services based on activity monitoring in ambient assisted living environments. In: 2014 IEEE Symposium on Intelligent Agents (IA) [Orlando, Florida, United States, 9-12 December 2014]: proceedings. New York, NY: IEEE, pp. 81-88. ISBN 9781479944897

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Abstract

Ambient Assisted Living (AAL) is considered as the main technological solution that will enable the aged and people in recovery to maintain their independence and a consequent high quality of life for a longer period of time than would otherwise be the case. This goal is achieved by monitoring human’s activities and deploying the appropriate collection of services to set environmental features and satisfy user preferences in a given context. However, both human monitoring and services deployment are particularly hard to accomplish due to the uncertainty and ambiguity characterising human actions, and heterogeneity of hardware devices composed in an AAL system. This research addresses both the aforementioned challenges by introducing 1) an innovative system, based on Self Organising Feature Map (SOFM), for automatically classifying the resting location of a moving object in an indoor environment and 2) a strategy able to generate context-aware based Fuzzy Markup Language (FML) services in order to maximize the users’ comfort and hardware interoperability level. The overall system runs on a distributed embedded platform with a specialised ceiling- mounted video sensor for intelligent activity monitoring. The system has the ability to learn resting locations, to measure overall activity levels, to detect specific events such as potential falls and to deploy the right sequence of fuzzy services modelled through FML for supporting people in that particular context. Experimental results show less than 20% classification error in monitoring human activities and providing the right set of services, showing the robustness of our approach over others in literature with minimal power consumption.

Item Type:	Chapter in book
Creators:	Acampora, G., Appiah, K., Hunter, A. and Vitiello, A.
Publisher:	IEEE
Place of Publication:	New York, NY
Date:	2014
ISBN:	9781479944897
Identifiers:	Number Type 10.1109/IA.2014.7009462 DOI
Divisions:	Schools > School of Science and Technology
Record created by:	EPrints Services
Date Added:	09 Oct 2015 10:37
Last Modified:	09 Jun 2017 13:34
URI:	https://irep.ntu.ac.uk/id/eprint/15644

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