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AMERICAN UNIVERSITY OF SHARJAH SCHOOL OF ENGINEERING COMPUTER SCIENCE & ENGINEERING Fuzzy Logic based Patients Monitoring System Presented by : Presented by : Student Name : Jumanah A. Al-Dmour Supervised by : Supervised by : Advisor : Prof. Abdulrahman Al-Ali Co-advisors : Prof. Assim Sagahyroon : Prof. Salah Abusnana : Prof. Salah Abusnana Fall 2012 / 2013 1

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Outlines IntroductionIntroduction General Problem Literature Review Software Architecture Overall System Design ConclusionConclusion Research Implementation and Results 2

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Introduction The number of older persons has tripled over the last 50 years; it will more than triple again over the next 50 years. The older population is growing faster than the total population in practically all regions of the worldand the difference in growth rates is increasing. Average annual growth rate of total population and population aged 60 or over [1] 3

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General Problem In general, life expectancy is increasing, this will lead to a tremendous increase in aging population. A much different set of expectations of quality of life and medical care. Many academic institutions and industrial organizations are engaged in healthcare research. Philips, Intel, GE Medical, IBM, Medtronic, and Carnegie Mellon. 4

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Literature Review Patients Monitoring Systems Patient monitoring system is a system that consists of various devices that are used to monitor and supervise patients and alerts if the patient gets into a critical state such as a heart monitor. Why Wireless? cost effectiveness. Provide a better Quality of Life. Patients mobility. User or patient's ability to view his/her medical data trends anywhere and anytime with minimum additional hardware requirements 5

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Literature Review Existing Wireless Technologies Zigbee-Based Solution Discrete RFID-Based Solution Bluetooth-Based Solution 6 WiFi-Based Solution

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Literature Review Fuzzy Logic Based Systems Blood glucose monitoring using Fuzzy logic A closed loop feedback system Continuously monitors the patients blood glucose level and adjusts the infusion of insulin to an optimal rate 7

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Overall System Design In this research, our goal is to: Design, build, and test a wireless data acquisition unit DAQ to collect patient vital signs while they are on the move. Develop a DAQ API and database to profile patients and save their medical records and health status. Develop a Fuzzy Logic algorithm based on the MEWS system to online analyze the patients vital signs and issue warnings and send alarm messages to caretaker in-case of any abnormality. Implement and test the proposed system. 8

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Overall System Design Functional Requirements AccessibilityScalability Security Software usability Safety Operational requirements Non-Functional Requirements Arranging readers in a specific arrangement and localizing their positions to a fixed dataset, Performing registration tasks, Collecting patients vital signs Storing data Alerting staff 9

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Overall System Design Overall System Architecture Based on the requirements, the proposed wireless monitoring system consists of five major building modules: 10

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Overall System Design The Mobile Data Acquisition Unit 11 The mobile data acquisition unit module consists of the RFID based vital signs sensors. The system consists of the following RFID based vital signs sensors: Blood Pressure Sensor Pulse Oxi-meter (SPO2) Body Temperature Sensor Blood Sugar (Glucose) Sensor

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Overall System Design 12

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Software Architecture 13

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Software Architecture 14

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Software Architecture 15

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Lotfi Zadeh The concept of Fuzzy Logic (FL) was first conceived by Lotfi Zadeh, a professor at the University of California at Berkley. [1] The concept was presented not as a control methodology, but as a way of processing data by allowing partial set membership rather than crisp set membership or NonMembership. Professor Zadeh reasoned that people do not require precise, numerical information input, and yet they are capable of highly adaptive control. If feedback controllers could be programmed to accept noisy, imprecise input, they would be much more effective and perhaps easier to implement. [1] Diversity Tech FPGA & BOARD DESIGN SERVICES Software Architecture The Fuzzy Logic System 16

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Crisp Set Vs. Fuzzy Set Crisp logic needs hard decisions. Like in this chart. In this example, anyone lower than 175 cm considered as short, and behind 175 considered as high. Someone whose height is 180 is part of TALL group, exactly like someone whose height is 190. Fuzzy Logic deals with membership in group functions. In this example, someone whose height is 180, is a member in both groups. Since his membership in group of TALL is 0.5 while in group of SHORT only 0.1, it may be seen that he is much more TALL than SHORT. Software Architecture The Fuzzy Logic System 17

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18 Typical Fuzzy Logic control implementation involving 3 steps: Fuzzification: Fuzzification: converting the crisp inputs to membership functions which comply to intuitive perception of system status. Rules Processing: Rules Processing: calculating the response from system status inputs according to the pre-defined rules matrix (control algorithm implementation). De-Fuzzification: De-Fuzzification: converting the Rules Processing results to crisp output/s to feed into the control devices. FuzzificationDe-Fuzzification Rule Proc. #1 Rule Proc. #2 Rule Proc. #3 Rule Proc. #4 .. Rule Proc. #n Sensors Inputs System Status Control Response Action Outputs Software Architecture The Fuzzy Logic System

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The Modified Early Warning Score (MEWS) is a tool for bedside evaluation of patients and is based on five physiological parameters: systolic blood pressure, pulse rate, respiratory rate, temperature, and AVPU score (A for 'alert', V for 'responsive to verbal stimulation', P for 'responsive to painful stimulation', U for 'unresponsive'). 19 Software Architecture The Modified Early Warning Score System Mews

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In this work, additional MEWS parameters were used in order to calculate the MEWS score. The parameters used are: Systolic Blood Pressure (SBP), Heart Rate (HR), Oxygen saturation (SPO2), Body Temperature (TEMP), and Blood Sugar (BS) Modified Early Warning Score MEWS Risk Band Low +3 Low +2 Low +1 Normal +0 High +1 High +2 High +3 Vital Sign Systolic Blood PressureSBP