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Intelligent assisted living framework for monitoring elders

Bastaki, B.B., BOSAKOWSKI, Tomasz and BENKHELIFA, Elhadj (2018) Intelligent assisted living framework for monitoring elders. Proceedings of IEEE/ACS International Conference on Computer Systems and Applications, AICCSA, 2017-O. pp. 495-500. ISSN 2161-5330

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Abstract or description

Recently, Ambient Intelligence Systems (AmI) in particular Ambient Assisted Living (AAL) are attracting intensive research due to a large variety of application scenarios and an urgent need for elderly in-home assistance. AAL is an emerging multi-disciplinary paradigm aiming at exploiting information and communication technologies in personal healthcare and telehealth systems for countering the effects of growing elderly population. AAL systems are developed to help elderly people living independently by monitoring their health status and providing caregivers with useful information. However, strong contributions are yet to be made on context binding of newly discovered sensors for providing dynamic or/and adaptive UI for caregivers, as the existing solutions (including framework, systems and platforms) are mainly focused on checking user operation history, browser history and applications that are most used by a user for prediction and display of the applications to an individual user. The aim of this paper is to propose a framework for making the adaptive UI from context information (real-time and historical data) that is collected from caregivers (primary user) and elderly people (secondary user). The collected data is processed to produce the contextual information in order to provide assistive services to each individual caregiver. To achieve this, the proposed framework collects the data and it uses a set of techniques (including system learning, decision making) and approaches (including ontology, user profiling) to integrate assistive services at runtime and enable their bindings to specific caregivers, in so doing improving the adaptability parameter of UI for the AAL. © 2017 IEEE.

Item Type: Article
Additional Information: Presented at The Conference of 14th IEEE/ACS International Conference on Computer Systems and Applications, AICCSA 2017 ; Conference Date: 30 October 2017 - 3 November 2017; Conference Code:135250
Faculty: School of Computing and Digital Technologies > Computing
Depositing User: Library STORE team
Date Deposited: 13 Jun 2018 15:33
Last Modified: 13 Jun 2018 15:33
URI: http://eprints.staffs.ac.uk/id/eprint/4417

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