Fuzzy Logic Based Patients' Monitoring System

Abstract

The ever increasing health care costs are becoming a major concern to both, individuals and authorities. This has tempted researchers to seek alternative models to the traditional and costly hospital-based monitoring and caring approach. One such an approach is the utilization of mobile units that allow for the remote observation and diagnosis of patients in their homes. Advances in VLSI circuits, single-chip embedded-system computing platforms, mobile telecommunications, and web services have provided valuable opportunities to enhance the design and performance of mobile patient's health monitoring platforms. In particular, Radio Frequency Identification (RFID) technology has emerged as one of the possible valuable solutions that can be utilized in future healthcare systems. RFID tags integrated with built-in vital signs sensors such as Body Temperature (TEMP), Blood Pressure (BP), Heart Rate (HR), Blood Sugar Level (BS) and Oxygen Saturation in Blood (SPO2) are useful in identifying and recording the state of a patient. In this work, we proposes the design, implementation, and testing of a mobile RFID-based health care system. The system consists of a wireless mobile vital signs data acquisition unit and a fuzzy-logic-based-software algorithm to monitors and assess patients' conditions on 24/7 bases. A set of fuzzy rules are developed to diagnose the monitored patient's status based on the received vital signs namely; TEMP, BP, HR, BS, and SPO2. The fuzzy algorithm will output an early warning of any patient's abnormality status. The system was implemented and tested at Rashid Center for Diabetes and Research (RCDR) hosted in Khalifa Hospital, Ajman, UAE using a representative sample of 26 patients. System performance is compared with the medically accepted standard, namely, the Modified Early Warning System (MEWS) that is currently widely used in practice. The proposed system has proven that it outperforms the MEWS system in many cases, and hence an indication of the usefulness of this fuzzy-based approach.