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Sensors 2016, 16(4), 531; doi:10.3390/s16040531

Towards A Self Adaptive System for Social Wellness

1
College of Technological Innovation, Zayed University, 144-534 Abu Dhabi, UAE
2
Department of Computer Engineering, Kyung Hee University, Yongin-Si 446-701, Korea
3
School of Engineering and Computing, University of the West of Scotland, Paisley, PA12BE, Scotland, UK
*
Author to whom correspondence should be addressed.
Academic Editors: Yunchuan Sun, Antonio Jara and Shengling Wang
Received: 15 February 2016 / Revised: 19 March 2016 / Accepted: 28 March 2016 / Published: 13 April 2016
(This article belongs to the Special Issue Identification, Information & Knowledge in the Internet of Things)
View Full-Text   |   Download PDF [3314 KB, uploaded 13 April 2016]   |  

Abstract

Advancements in science and technology have highlighted the importance of robust healthcare services, lifestyle services and personalized recommendations. For this purpose patient daily life activity recognition, profile information, and patient personal experience are required. In this research work we focus on the improvement in general health and life status of the elderly through the use of an innovative services to align dietary intake with daily life and health activity information. Dynamic provisioning of personalized healthcare and life-care services are based on the patient daily life activities recognized using smart phone. To achieve this, an ontology-based approach is proposed, where all the daily life activities and patient profile information are modeled in ontology. Then the semantic context is exploited with an inference mechanism that enables fine-grained situation analysis for personalized service recommendations. A generic system architecture is proposed that facilitates context information storage and exchange, profile information, and the newly recognized activities. The system exploits the patient’s situation using semantic inference and provides recommendations for appropriate nutrition and activity related services. The proposed system is extensively evaluated for the claims and for its dynamic nature. The experimental results are very encouraging and have shown better accuracy than the existing system. The proposed system has also performed better in terms of the system support for a dynamic knowledge-base and the personalized recommendations. View Full-Text
Keywords: activity recognition; change management; u-healthcare; decision support system; service recommendation activity recognition; change management; u-healthcare; decision support system; service recommendation
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Khattak, A.M.; Khan, W.A.; Pervez, Z.; Iqbal, F.; Lee, S. Towards A Self Adaptive System for Social Wellness. Sensors 2016, 16, 531.

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