Abstract
Recently, mobile devices have dramatically improved their communications and processing capabilities, so enabling the possibility of embedding knowledge-based decision support components within Remote Health Monitoring (RHM) applications for the ubiquitous and seamless management of chronic patients. According to these considerations, this paper presents a light-weight, rule-based, reasoning system, purposely designed and optimized to build knowledge-based Decision Support Systems efficiently embeddable in mobile devices. The key issues of such a system are both a domain-independent reasoning algorithm and knowledge representation capabilities, specifically thought for both computation intensive and real-time RHM scenarios. The performance evaluation of the proposed system has been arranged according to the Taguchi’s experimental design and performed directly on a mobile device in order to quantitatively assess its effectiveness in terms of memory usage and response time. Moreover, a case study has been arranged in order to evaluate the effectiveness of the proposed system within a real RHM application for monitoring cardiovascular diseases. The evaluation results show that the system offers an innovative and efficient tool to build mobile DSSs for healthcare applications where real-time performance or computation intensive demands have to be met.
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