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NON-INVASIVE BLOOD GLUCOSE
MONITORING SYSTEM
Alsamad Ansari (54931) Rishabh Posti (54952) Ritik Goyal (54955)
Simmy (54956) Sonakshi Arya (54957)
Project-II (495-B)
Guided By – Dr. Paras
LIST OF CONTENTS
01
Introduction
0 2
Current
Challenges
03
Hardware and
Software
Requirements
06
Benefits
05
Implementation
04
How it
Works?
07
Future
Development
08
Conclusion
INTRODUCTION
• Diabetes, a common chronic disease affecting millions worldwide, requires
regular monitoring of blood glucose levels.
• However, the current invasive method, involving finger pricks, poses several
challenges such as pain, expense, and the potential spread of infectious
diseases.
• Moreover, long-term use of the invasive method can lead to tissue damage in
the fingers.
• By utilizing the variation in the intensity of NIR (Near-Infrared) light
received from the finger, we can accurately determine the glucose
level in the blood.
Near-Infrared Spectroscopy Range
Current Challenges
• Invasiveness and Discomfort
• Limited Continuous Monitoring
• Cost and Accessibility
• Usability and applicability
challenges
• Time Consuming
ARDUINO UNO (ATMEGA328P) LCD 16x2 DISPLAY
HARDWARE REQUIREMENTS
IR SENSOR MODULE PHOTODIODE (1450nm,1550nm)
IR LED (1450nm,1550nm) GLUCOMETER
Arduino IDE Python IDE (Google Colab)
SOFTWARE REQUIREMENTS
HOW IT WORKS?
• This project involves non-invasive monitoring of glucose using NIR
and pulse
sensor.
• Near-infrared spectroscopy is a method that uses the near-infrared
region of
electromagnetic spectrum.
• The basic pulse sensor consists of a light emitting diode and a
detector like a light detecting resistor or a photodiode.
• When a tissue is illuminated with the light source i.e. light emitted by
the led, the amount of light absorbed depends on the blood volume in
that tissue.
• The detector output is in the form of electrical signal and is
proportional to the pulse rate.
• We determine the blood glucose level by passing NIR radiation through a
region of the body we are interested in to monitor its glucose level.
• As light source, NIR LED from Thorlabs is used, with λ = 1300nm,
1450nm, and 1550nm.
• The correlation between absorbed radiation and glucose concentration id
determined by Beer Lambert Law.
• Photodiode voltage is proportional to near infrared light transmittance. It
is then correlated with blood glucose concentration.
BLOCK DIAGRAM
CIRCUIT DIAGRAM
Circuit Diagram Of Non-Invasive Blood Glucose Monitoring System
IMPLEMENTATION
DATA COLLECTION
• We conducted a study involving diabetic individuals to analyze the
relationship between their glucose levels and the corresponding
analog voltage readings.
• Glucose levels were measured using the invasive laboratory method
for all participants.
• Simultaneously, analog voltage readings were obtained using the
proposed hardware setup
CURVE FITTING
• To establish the relationship between glucose levels and analog
voltage, we employed polynomial regression analysis.
• We performed curve fitting by fitting polynomials of different orders
(1st to 5th) to the data.
• The evaluation helped us select the polynomial regression equation
that provided the best balance between accuracy and complexity.
• After evaluating the performance of the polynomial regression models
of different orders, we observed the following:
• The first-order polynomial (linear) had limited accuracy in capturing
the non-linear relationship between glucose levels and analog
voltage.
F(x) = 0.677*x -
107.11
• As the order of the polynomial increased, the models could capture
more complex relationships, potentially improving prediction accuracy.
F(x) = 665.091*x - 2.843*x*x + 0.005*x*x*x -
.0000044031*x*x*x*x + .0000000002407*x*x*x*x*x -
58324.419;
RESULT
• Root Mean Square Error = Sqrt((Sum of Square of Individual Value)/Total No. of Sets)
Root Mean Square Error = 22.80%
ARDUINO IMPLEMENTATION
BENEFITS
• Convenience and Comfort
• Improved Quality of Life
• Enhanced Compliance
• Real-Time Monitoring
• Early Detection of Highs and Lows
• Reduced Risk of Infections
• User Friendly Experience
FUTURE
DEVELOPMENT
• Artificial Intelligence Integration
• Wearable Technology
• Continuous Monitoring
• Enhanced Connectivity
• Predictive Analytics
• Personalized Recommendations
• Improved Sensor Technology
• Collaboration and Partnerships
CONCLUSION
• The research successfully demonstrated a strong relationship between the
sensor output voltage and glucose concentration through experiments.
• The proposed non-invasive glucose monitoring system showed good accuracy
and has low manufacturing and maintenance costs.
• The results of the prototype indicate a promising future for the implementation of
NIR technology in real-time and continuous non-invasive glucose monitoring.
• The proposed NIR spectroscopy experiment has great potential for non-invasive
continuous monitoring of glucose levels in the human body.
• Future studies will investigate the impact of variables such as skin roughness
and body fluid concentration to further improve calibration and system sensitivity.
REFRENCES
• https://www.e-dmj.org/journal/view.php?doi=10.4093/dmj.2019.0121
• https://www.researchgate.net/publication/323526055_Design_of_no n-
invasive_glucose_meter_using_near-infrared_technique
• https://iopscience.iop.org/article/10.1088/1742-6596/1361/1/012041
• Sherman M, “How do blood glucose meters work?,” Chem, vol. 13,
(2006).[Online].NewsAvailable:https://uwaterloo.ca/chem13news/sites/ca.
chem13news/files/uploads/files/343_dec_2006_pages_5-6.pdf.
• https://www.researchgate.net/publication/323526055_Design_of_no n-
invasive_glucose_meter_using_near-infrared_technique.
• https://chat.openai.com/?model=text-davinci-002-render-sha
THANK YOU

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NON INVASIVE GLUCOSE BLODD MONITORING SYSTEM (1) (2) (1).pptx

  • 1. NON-INVASIVE BLOOD GLUCOSE MONITORING SYSTEM Alsamad Ansari (54931) Rishabh Posti (54952) Ritik Goyal (54955) Simmy (54956) Sonakshi Arya (54957) Project-II (495-B) Guided By – Dr. Paras
  • 2. LIST OF CONTENTS 01 Introduction 0 2 Current Challenges 03 Hardware and Software Requirements 06 Benefits 05 Implementation 04 How it Works? 07 Future Development 08 Conclusion
  • 3. INTRODUCTION • Diabetes, a common chronic disease affecting millions worldwide, requires regular monitoring of blood glucose levels. • However, the current invasive method, involving finger pricks, poses several challenges such as pain, expense, and the potential spread of infectious diseases. • Moreover, long-term use of the invasive method can lead to tissue damage in the fingers.
  • 4. • By utilizing the variation in the intensity of NIR (Near-Infrared) light received from the finger, we can accurately determine the glucose level in the blood. Near-Infrared Spectroscopy Range
  • 5. Current Challenges • Invasiveness and Discomfort • Limited Continuous Monitoring • Cost and Accessibility • Usability and applicability challenges • Time Consuming
  • 6. ARDUINO UNO (ATMEGA328P) LCD 16x2 DISPLAY HARDWARE REQUIREMENTS
  • 7. IR SENSOR MODULE PHOTODIODE (1450nm,1550nm) IR LED (1450nm,1550nm) GLUCOMETER
  • 8. Arduino IDE Python IDE (Google Colab) SOFTWARE REQUIREMENTS
  • 9. HOW IT WORKS? • This project involves non-invasive monitoring of glucose using NIR and pulse sensor. • Near-infrared spectroscopy is a method that uses the near-infrared region of electromagnetic spectrum. • The basic pulse sensor consists of a light emitting diode and a detector like a light detecting resistor or a photodiode. • When a tissue is illuminated with the light source i.e. light emitted by the led, the amount of light absorbed depends on the blood volume in that tissue. • The detector output is in the form of electrical signal and is proportional to the pulse rate.
  • 10. • We determine the blood glucose level by passing NIR radiation through a region of the body we are interested in to monitor its glucose level. • As light source, NIR LED from Thorlabs is used, with λ = 1300nm, 1450nm, and 1550nm. • The correlation between absorbed radiation and glucose concentration id determined by Beer Lambert Law. • Photodiode voltage is proportional to near infrared light transmittance. It is then correlated with blood glucose concentration.
  • 12. CIRCUIT DIAGRAM Circuit Diagram Of Non-Invasive Blood Glucose Monitoring System
  • 14. DATA COLLECTION • We conducted a study involving diabetic individuals to analyze the relationship between their glucose levels and the corresponding analog voltage readings. • Glucose levels were measured using the invasive laboratory method for all participants. • Simultaneously, analog voltage readings were obtained using the proposed hardware setup
  • 15. CURVE FITTING • To establish the relationship between glucose levels and analog voltage, we employed polynomial regression analysis. • We performed curve fitting by fitting polynomials of different orders (1st to 5th) to the data. • The evaluation helped us select the polynomial regression equation that provided the best balance between accuracy and complexity.
  • 16. • After evaluating the performance of the polynomial regression models of different orders, we observed the following: • The first-order polynomial (linear) had limited accuracy in capturing the non-linear relationship between glucose levels and analog voltage. F(x) = 0.677*x - 107.11
  • 17. • As the order of the polynomial increased, the models could capture more complex relationships, potentially improving prediction accuracy. F(x) = 665.091*x - 2.843*x*x + 0.005*x*x*x - .0000044031*x*x*x*x + .0000000002407*x*x*x*x*x - 58324.419;
  • 18. RESULT • Root Mean Square Error = Sqrt((Sum of Square of Individual Value)/Total No. of Sets) Root Mean Square Error = 22.80%
  • 20. BENEFITS • Convenience and Comfort • Improved Quality of Life • Enhanced Compliance • Real-Time Monitoring • Early Detection of Highs and Lows • Reduced Risk of Infections • User Friendly Experience
  • 21. FUTURE DEVELOPMENT • Artificial Intelligence Integration • Wearable Technology • Continuous Monitoring • Enhanced Connectivity • Predictive Analytics • Personalized Recommendations • Improved Sensor Technology • Collaboration and Partnerships
  • 22. CONCLUSION • The research successfully demonstrated a strong relationship between the sensor output voltage and glucose concentration through experiments. • The proposed non-invasive glucose monitoring system showed good accuracy and has low manufacturing and maintenance costs. • The results of the prototype indicate a promising future for the implementation of NIR technology in real-time and continuous non-invasive glucose monitoring. • The proposed NIR spectroscopy experiment has great potential for non-invasive continuous monitoring of glucose levels in the human body. • Future studies will investigate the impact of variables such as skin roughness and body fluid concentration to further improve calibration and system sensitivity.
  • 23. REFRENCES • https://www.e-dmj.org/journal/view.php?doi=10.4093/dmj.2019.0121 • https://www.researchgate.net/publication/323526055_Design_of_no n- invasive_glucose_meter_using_near-infrared_technique • https://iopscience.iop.org/article/10.1088/1742-6596/1361/1/012041 • Sherman M, “How do blood glucose meters work?,” Chem, vol. 13, (2006).[Online].NewsAvailable:https://uwaterloo.ca/chem13news/sites/ca. chem13news/files/uploads/files/343_dec_2006_pages_5-6.pdf. • https://www.researchgate.net/publication/323526055_Design_of_no n- invasive_glucose_meter_using_near-infrared_technique. • https://chat.openai.com/?model=text-davinci-002-render-sha