Design and evaluation of a cloud-based Mobile Health Information Recommendation system on wireless sensor networks. (January 2016)

• Record Type: Journal Article
• Authors: Wang, Shu-Lin; Chen, Young Long; Kuo, Alex Mu-Hsing; Chen, Hung-Ming; Shiu, Yi Shiang
• Is Part Of: Computers & electrical engineering. Volume 49(2016)
• Page Start: 221
• Page End: 235
• Publication Date: 2016-01
• Subjects: Health information technology (HIT) -- Mobile web service -- Collaborative recommender -- Wireless sensor network -- Cloud-computing; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2015.07.017
• Languages: English
• ISSNs: 0045-7906

Abstract

Graphical abstract: Highlights: The development of a Mobile Health Information Recommendation system which integrates Cloud Computing, and Context-aware technology, and applies Collaborative Filtering technique for information recommendation. Through the collaborative recommender, users can effectively obtain consistent health information, and reduce energy and time consumption. Abstract: Recent technological trends such as cloud computing, wireless communication, and wireless sensor networks provide a strong infrastructure and offer a true enabler for health information technology services over the Internet. This system is based on the cloud computing environment, integrating mobile communication technology, context-aware technology, and wireless sensor networks to build a mobile web for a personalized health information service, which includes two health information recommendation service functions: a collaborative recommender and a physiological indicator-based recommender. We further propose a hybrid predictive model, which combines the Grey Theory and Markov chain to predict the moving object's path. This will decrease the cost which arises from tracking errors and prolong the network's lifetime. From the experiment results of usability, it has been discovered that subjects have positive responses towards usability measurement dimensions of the system: satisfaction, expectation-confirmation, perceived trust, perceived usefulness, and perceived value.