MICROCONTROLLER-BASED HUMAN STRESS DETECTION SYSTEM

USING FUZZY LOGIC

AWAIS GUL AIRIJ

A project report submitted in partial fulfilment of the

requirements for the award of the degree of

Master of Engineering (Electronics & Telecommunication)

Faculty of Electrical Engineering

Universiti Teknologi Malaysia

DECEMBER 2015

iii

DEDICATION

Gratefully dedicated to my family, my mentor and friends. I couldn’t have completed

this without you all. Thank you for your love, support and guidance.

iv

ACKNOWLEDGEMENT

I am indeed grateful to Almighty ALLAH for providing me the opportunity

and strength to commence and conclude my Master of Engineering at Universiti

Teknologi Malaysia.

It is a great pleasure to express my heart-felt gratitude to my supervisor Dr

Shaikh Nasir Bin Shaikh Husin for his inspirational guidance, kind supervision and

comprehensive advice throughout the duration of my work.

I wish to express my deepest appreciation to my previous supervisor Dr

Rabia Bakhteri. The present work would not have been possible without her

encouragement and support. I thank Dr Rabia Bakhteri for her detailed and insightful

feedback, which helped me shape this research. It was a great honour to work under

her supervision for nine months. However, she had to move to Canada for some

reason and she could not help me complete this research under her supervision.

Finally, I would like to thank my father Muhammad Yaqoob Memon, my

brother Naeem Aziz Memon along with my other family members and friends for

their encouragement throughout my work. I would not have been able to complete

this without them. I also would like to appreciate all those who have helped me

directly or indirectly towards the completion of this project.

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ABSTRACT

This research presents a working prototype of human stress detection

device capable of measuring human stress level perhaps also for autistic children.

The device records human physiological signals in order to determine the mental

stress level. High stress levels may be dangerous especially for certain individuals

such as autistic children who are unable to express mounting levels of stress. The

autistic children can have frequent tantrums and seizure activities without any visible

signs or symptoms, making this device a useful tool for parents and doctors to

anticipate any harmful behaviours of autism. This research focuses on the hardware

and software development of a low cost microcontroller-based stress detection

system prototype. The prototype was designed using Arduino Mega platform and

tested with 35 clinical patients. The data from two sensors is fed to the

microcontroller using its two analog input pins and the sensor data is sent to the

fuzzy logic module which is pre-programmed into the microcontroller for further

processing. The output of the prototype is displayed on the LCD module connected

to five digital pins of the microcontroller. In addition, three LEDs are connected to

three digital pins of the microcontroller which light up in accordance with the stress

levels. In order to test the developed system, an experiment was designed which

requires subjects to perform mental calculations to solve arithmetic problems. The

experiment involves three phases: low stress phase (P-1), medium stress phase (P-2)

and high stress phase (P-3). The results showed that the prototype measures the stress

levels with high degree of accuracy and efficiency. Apart from that, the results also

highlighted that the stress neither depends on age nor gender.

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ABSTRAK

Kajian ini membentangkan prototaip alat pengesan tekanan minda manusia

yang digunakan untuk mengukur tahap tekanan manusia dan juga untuk kanak-kanak

autistik. Peranti berfungsi untuk merakam isyarat fisiologi manusia bagi menentukan

kadar tekanan. Tahap tekanan yang tinggi mungkin berbahaya terutamanya untuk

individu tertentu seperti kanak-kanak autistik dimana mereka tidak mampu untuk

menyatakan dengan jelas perasaan atau tekanan yang dihadapi. Kanak-kanak autistik

boleh mempunyai tantrum yang kerap dan bersikap agresif secara drastik tanpa

sebarang tanda yang boleh dikesan dengan awal, menjadikan peranti ini alat yang

berguna untuk ibu bapa dan doktor untuk menjangka tingkah laku yang berbahaya

dari penghidap autisme. Kajian ini memberi tumpuan kepada pembangunan

perkakasan dan perisian mikropengawal kos rendah berasaskan prototaip sistem

pengesan tekanan. Prototaip ini telah direka menggunakan platform ‘Arduino Mega’

dan kemudian diuji dengan 35 orang pesakit klinikal. Data daripada dua sensor

disalurkan ke mikropengawal dengan menggunakan dua pin input analog dan data

tersebut dihantar ke modul logik kabur (Fuzzy Logic) yang telah diprogramkan

dalam mikropengawal untuk proses seterusnya. Output prototaip ini dipaparkan pada

modul paparan kristal cecair (LCD) yang disambungkan dengan lima pin digital

mikropengawal. Di samping itu, tiga diod pemancar cahaya (LED) disambungkan

kepada tiga pin digital mikropengawal yang menyala mengikut tahap tekanan. Bagi

menguji prototaip yang dibangunkan, pesakit dikehendaki menyelesaikan masalah

aritmetik dengan melakukan teknik pengiraan secara mental atau congak.

Eksperimen ini melibatkan tiga fasa iaitu fasa tekanan rendah (P-1), fasa tekanan

sederhana (P-2) dan fasa tekanan tinggi (P-3). Keputusan menunjukkan bahawa

tahap tekanan dapat diukur dengan tepat dan cekap. Selain itu, kajian ini

merumuskan bahawa tekanan tidak bergantung kepada umur atau jantina.

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TABLE OF CONTENTS

CHAPTER TITLE PAGE

DECLARATION ii

DEDICATION iii

ACKNOWLEDGEMENT iv

ABSTRACT v

ABSTRAK vi

TABLE OF CONTENTS vii

LIST OF TABLES x

LIST OF FIGURES xi

LIST OF ABBREVIATIONS xiii

LIST OF APPENDICES xiv

1 INTRODUCTION 1

1.1 Project Background 1

1.2 Problem Statement 2

1.3 Research Objectives 3

1.4 Research Scope 3

1.5 Research Contributions 3

1.6 Thesis Organization 3

2 LITERATURE REVIEW 5

2.1 Introduction 5

2.2 Project Background 5

2.2.1 Case Studies on Autism Spectrum Disorder 6

2.3 Types of Stress 7

2.3.1 Acute Stress 7

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2.3.2 Episodic Acute Stress 7

2.3.3 Chronic Stress 8

2.4 Sensors/Devices 8

2.4.1 Galvanic Skin Response Sensor 8

2.4.2 Pulse Sensor/Blood Volume Pulse 9

2.4.3 Temperature Sensor 9

2.4.4 ASL 504 Eye Gaze Tracker 10

2.4.5 Peizoelectric Film Sensor 10

2.4.6 Accelerometer 11

2.4.7 EEG Sensor 11

2.4.8 Force Sensor 12

2.5 Artificial Intelligence 12

2.5.1 Fuzzy Logic 12

2.5.2 Bayes Net Classifier 13

2.5.3 Support Vector Machine 13

2.5.4 Decision Tree 14

2.6 Related Literature 14

3 RESEARCH METHODOLOGY 17

3.1 Introduction 17

3.2 Sensors 18

3.2.1 Working Principle of Heartbeat / Pulse Sensor 19

3.2.2 Working Principle of Galvanic Skin Response Sensor 19

3.3 LCD Module 20

3.4 Arduino Mega Board 20

3.5 Indicator 21

3.6 Moving Average 21

3.7 Decision-Making Module 22

3.8 Data Collection 23

4 HARDWARE DESIGN 24

4.1 Interfacing Pulse Sensor with Arduino 24

4.2 Interfacing GSR Sensor with Arduino 25

4.3 Interfacing LCD Module with Arduino 27

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4.4 Arduino Pinouts 28

4.5 Fuzzy Logic Module with Arduino 29

4.5.1 Fuzzy Logic Rules 29

4.5.2 Membership Function 29

4.6 Final Prototype 31

5 RESULTS AND DISCUSSION 32

5.1 Results 33

5.2 Discussion 34

6 CONCLUSION AND FUTURE RECOMMENDATIONS 36

6.1 Conclusion 36

6.2 Future Recommendations 36

REFERENCES 37

Appendices A - B 40-56

x

LIST OF TABLES

TABLE NO. TITLE PAGE

2.1 Summary of literature review 16

4.1

Pin functions of LCD module 27

xi

LIST OF FIGURES

FIGURE NO. TITLE PAGE

1.1

2.1

2.2

2.3

2.4

2.5

2.6

2.7

2.8

2.9

3.1

3.2

3.3

3.4

3.5

3.6

4.1

4.2

4.3

4.4

4.5

Stress Response to a Stressor (stimuli)

The reaction of stress factors is governed by ANS

GSR sensor

Pulse sensor

Temperature sensor

ASL eye gaze tracker

Piezo-electric film sensor

Accelerometer

Device to record brain waves

Force-sensing resistor

System architecture

Project flowchart

Working principle of photoplethysmography

Arduino Mega board

LEDs as indicators

Fuzzy decision-making

Heart rate (pulse) sensor by pulsesensor.com

Testing heart rate sensor with Arduino

Grove GSR sensor

Testing grove GSR sensor with Arduino

48x84 graphic LCD module

1

6

8

9

9

10

10

11

11

12

17

18

19

21

21

22

24

25

26

26

27

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4.6

4.7

4.8

4.9

4.10

5.1

5.2

5.3

5.4

Pin connections between Arduino & LCD module

Membership function curve (trapezoidal) for low stress

Membership function curve (trapezoidal) for med stress

Membership function curve (trapezoidal) for high stress

Final prototype of stress detection system

Working of human SDS prototype

Stress levels in males for all three phases

Stress levels in females for all three phases

Testing of LM35 temperature sensor with Arduino

28

30

30

30

31

32

33

34

35

xiii

LIST OF ABBREVIATIONS

ANS - Autonomic Nervous System

ASD - Autism Spectrum Disorder

BVP - Blood Volume Pulse

ECG - Electrocardiography

EEG - Electroencephalography

eFLL - Embedded Fuzzy Logic Library

FLM - Fuzzy Logic Module

GSR - Galvanic Skin Response

LCD - Liquid Crystal Display

MRI - Magnetic Resonance Imaging

PPG - Photoplethysmography

REI - Rhythmic Entertainment Intervention

SC - Skin Conductance

SDS - Stress Detection System

SVM - Support Vector Machine

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LIST OF APPENDIX

APPENDIX TITLE PAGE

A Arduino Code for Stress Detection System 63

B Evaluation Form 63

CHAPTER 1

INTRODUCTION

The present research aims at developing a working prototype in order to

detect stress by using Arduino Mega. The device makes use of human physiological

signals measured with the help of various electronic sensors, along with a fuzzy logic

decision-making system to calculate the stress levels accurately.

1.1 Project Background

The term “stress” basically refers to the physical reaction of a person in

response to an unpleasant/dangerous environmental condition or stimuli [1] as shown

in figure 1.1. Over-stress has proven to be one of the major factors contributing to

several serious health problems such as high blood pressure, hypertension,

cardiovascular diseases and panic attacks [2].

Figure 1.1: Stress response to a stressor (stimuli)

According to a research, stress is categorized into three types namely; acute

stress, episodic acute stress and chronic stress [3]. Acute stress and episodic acute

stress do not cause much damage as they are for a short period making people

anxious and frightened but are easily treatable [3]. However, on the other hand, the

2

chronic stress lasts longer and causes serious complications depending upon the type

of patients [4, 5]. As a result of stress, stress-response hormones such as epinephrine,

norepinephrine and cortisol are secreted which can be a contributing factor in cardiac

abnormalities[5] along with some other serious complications. Moreover, high levels

of stress can cause commonly known mental disorders namely; depression and

anxiety[4].

The system proposed in this project can non-invasively detect stress levels in

humans through some physiological signals such as skin conductance, temperature,

heart rate and blood pressure measured with the help of various electronic sensors.

1.2 Problem Statement

Nowadays, everybody is stressed, however, the type of stress dealt within the

very research and complications caused by it are limited to a specific group of

people. The children suffering from Autism Spectrum Disorder (ASD) are the focus

of study. According to a research, every 1 in 166 births suffers from ASD [6].

Autistic children can have seizures, panic attacks, suicide thoughts, commit homicide

and can also be harmful to their surroundings when they are under stress. But there

are no visible signs or warnings when under high stress.

Currently, low cost commercial device for detecting stress is not available.

Nonetheless, devices to monitor temperature, heart rate and variations in respiration

exist. Furthermore, even such high-end devices are devoid of detecting stress on their

own. For instance heart rate variation alone is incapable of detecting stress. There is

a dire need for a stand-alone device that can detect stress by using a couple of

physiological signals and process them further in order to decide whether the person

is under stress or not. Such device will possibly help the parents and doctors to

anticipate harmful behaviours of autism.

3

1.3 Research Objective

The objectives of this research are:

1) To study the fuzzy logic and familiarize with embedded fuzzy logic library

(eFLL) for Arduino.

2) To interface electronic sensors with Arduino Mega board.

3) To measure physiological signals accurately using electronic sensors.

4) To develop a prototype of human stress detection system.

5) To test the prototype functionality on intended subjects.

1.4 Research Scope

The scope of this research covers development of a human stress detection

system prototype consisting of Arduino Mega 2560 microcontroller board, LCD

display, pulse sensor, grove GSR sensor and LEDs as indicators. AtMega2560

microcontroller is the main processing unit for the entire system and is programmed

using Arduino IDE.

1.5 Research Contributions

Prior research has shown various approaches of stress detection but

unfortunately, there is no device available till today which is accurate and low cost.

This research aims to develop a prototype of human stress detection system which is

capable of accurate measurement of human stress levels at a very low cost.

1.6 Thesis Organization

This thesis consists of six chapters. The first chapter discusses the

background, problem statement, intended objectives and the scope of this research.

4

Chapter 2 includes the literature review focused on techniques, experimental setup,

results and drawbacks found in previously built devices for stress detection purpose.

In chapter 3, the working principles of the sensors and other components are

discussed. Later in chapter 4 the design of the system is discussed in terms of

interfacing the sensors, designing output display along with an indicator and

designing of the fuzzy logic system. The next chapter presents the results obtained

from the developed stress detection prototype and discussion on the results. The

problems and challenges faced during this research are also included in chapter 5.

Finally, a conclusion of the entire research and some recommendations for future

improvements are given in chapter 6.

37

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