Mobile ontology-based reasoning & feedback health monitoring system.

摘要

As medical technology has developed over the past few decades, the average human life-span has increased and as a side effect, chronic illness has become more prevalent. Many patients now require constant, everyday monitoring of their vitals to ensure their health conditions don't deteriorate. Such monitoring is often carried out in a hospital or hospital-like environment when it need not be. In more recent years, many research efforts have been made to develop robust health monitoring systems. Most of these systems are being used in hospitals to monitor patients' vitals in order to provide timely treatment. Introducing a ubiquitous system to remotely monitor patients outside of the hospital provides huge financial savings to the health care system, as non-critical patients who have partial autonomy and mobility can then be discharged from the hospital. This research describes a mobile ontology-based health monitoring system that seamlessly integrates vital data from various health sensors in order to identify the health status of the patient and to take the appropriate actions for a timely support.;Keywords: Healthcare, Telemedicine, Mobile Computing, Semantic Web, Ontology, Ontology-based Reasoning, Ubiquitous Computing, Web Services, Client-Server, Knowledge Management, Context Modelling.;The proposed approach makes use of recent advances in semantic web, ubiquitous computing, and mobile communication technologies to build a Mobile Ontology-based Reasoning and Feedback (MORF) health monitoring system. MORF includes a wearable monitor that uses various sensors to record a patient's vitals. To make MORF mobile, the sensed data, once acquired, is routed through a mobile device such as a cell phone or personal digital assistant (PDA) before being transmitted to the local monitoring station for analysis. This analysis is designed to embed a sense of context awareness into the system which enables it to understand its environmental circumstances and act based on that understanding. Using ontology-based reasoning, the server at the monitoring station manages properly the system's overall context and decides what actions to perform if the patient's vitals exceed their prescribed norms. The goal of MORF is to expand above, improve upon and advance beyond the many systems that currently exist in this field. Such advancements include the MORF's feedback capabilities which allow for the controlling of the frequency at which the sensed data is acquired. Based on the previously sensed data and the patient's current condition, the reasoning engine will tune the sensory data acquisition rate of the various sensors to provide the most effective monitoring possible. These are among the main contributions of the proposed MORF system whose aims are delivering context-aware intelligent autonomous services for healthcare monitoring.