AMERICAN UNIVERSITY OF SHARJAH SCHOOL OF ENGINEERING COMPUTER SCIENCE & ENGINEERING Fuzzy Logic...
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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