Design Of Motion Detection Using Tri-Axial Accelerometer For The Elderly.
i
DESIGN OF MOTION DETECTION USING TRI-AXIAL
ACCELEROMETER FOR THE ELDERLY
LIM HOK HONG
This Report Is Submitted In Partial Fulfillment of Requirement For The
Bachelor Degree of Electronic Engineering (Industrial Electronic)
Fakulti Kejuruteraan Elektronik dan Kejuruteran Komputer
Universiti Teknikal Malaysia Melaka
JUNE 2016
ii
DECLARATION
UNIVERSTI TEKNIKAL MALAYSIA MELAKA
FAKULTI KEJURUTERAAN ELEKTRONIK DAN KEJURUTERAAN KOMPUTER
BORANG PENGESAHAN STATUS LAPORAN
PROJEK SARJANA MUDA II
Tajuk Projek
:
DESIGN OF MOTION DETECTION USING TRI-AXIAL ACCELEROMETER FOR
THE ELDERLY
Sesi Pengajian
:
1
5
/
1
6
Saya LIM HOK HONG mengaku membenarkan Laporan Projek Sarjana Muda ini disimpan di Perpustakaan
dengan syarat-syarat kegunaan seperti berikut:
1. Laporan adalah hakmilik Universiti Teknikal Malaysia Melaka.
2. Perpustakaan dibenarkan membuat salinan untuk tujuan pengajian sahaja.
3. Perpustakaan dibenarkan membuat salinan laporan ini sebagai bahan pertukaran antara institusi
pengajian tinggi.
4. Sila tandakan ( √ ) :
SULIT*
TERHAD**
*(Mengandungi maklumat yang berdarjah keselamatan atau
kepentingan Malaysia seperti yang termaktub di dalam AKTA
RAHSIA RASMI 1972)
**(Mengandungi maklumat terhad yang telah ditentukan oleh
organisasi/badan di mana penyelidikan dijalankan)
TIDAK TERHAD
Disahkan oleh:
__
_________________________
(TANDATANGAN PENULIS)
Tarikh: ………………………..
___________________________________
(COP DAN TANDATANGAN PENYELIA)
Tarikh: ………………………..
iii
DEDICATION
“I hereby declare that the work in this project is my own except for summaries and
quotations which have been duly acknowledge.”
Signature
: ....................................................
Author
: LIM HOK HONG
Date
: 15TH JUNE 2016
iv
“I acknowledge that I have read this report and in my opinion this report is sufficient in
term of scope and quality for the award of Bachelor of Electronic Engineering
(Industrial Electronics) with Honours.”
Signature
: .........................................................
Supervisor’s Name
: DR. ANIS SUHAILA BINTI MOHD ZAIN
Date
: 15TH JUNE 2016
v
A very special dedication for my beloved family especially to my parents, Lim Kui Seng
and Liong Siu Moi.
Also for my gracious supervisor Dr. Anis Suhaila Binti Mohd Zain.
vi
ACKNOWLEDGEMENT
Thank God for the blessings and grace - I could complete this meaningful final
year project for two semester at Universiti Teknikal Malaysia Melaka (UTeM) and to
complete this FYP thesis. Special thanks also given to Faculty of Electronic and Computer
Engineering (FKEKK) for give us chances to gain a lot of useful knowledge and skill.
Besides that, faculty also provides many facilities for us when doing our project.
Apart from that, the person that I would like to express the deepest appreciation is
my project supervisor, Dr. Anis Suhaila Binti Mohd Zain. I am the lucky person who
supervised under Dr Anis because madam is a very caring and brilliant guidance that
always lead us to a solution. She had gave me a hand when I am facing different kind of
difficulties and challenging.
In addition, I would like to thank my friends and family who always help
me in term of mentally or physically support. Without them, sure I am not able to
accomplish my project with flying colors within this two semester. Last but not least, I
would like to offer my regards and blessings to all of those who supported me in any
respect during the completion of my final year project.
vii
ABSTRACT
Falls are a major public health problem among older people nowadays. Most of
the fall detection device is still using the Personal Emergency Response System (PERS)
which is not function automatically when a fall is detected. This issue will cause severe
injuries or even death when the elderly loss of consciousness or faint after a fall. But in
my project, this problem has been solved by using the automatically fall detection system
by using motion sensor. The goal of this project is to design, construct and analyze the
system by using the tri-axial accelerometer sensor and to develop a portable fall detection
system, which is capable of automatically discriminating between falls and Activities of
Daily Life (ADL). The hardware part of fall detection system is divided into two part.
First part is for data acquisition in order to differentiate the acceleration of different kind
of activity while another part is for the fall alert system which is capable to sending the
SMS when a fall is detected. To extract the features of falls, falls data and ADL data
obtained from young subjects are analyzed. The result will be collect from the different
signal that produced by the accelerometer. From the experiment that has been carried out,
it shows that the fall detection system can accomplished an accuracy of 96.11%. It can be
concluded that, the automatically fall detection system will being a good supporting
device for the elderly. So, the elderly citizen can get the medical help when the
accidentally fall is happened.
viii
ABSTRAK
Jatuh adalah masalah kesihatan awam terutamanya di kalangan orang yang lebih
tua. Kebanyakan peranti pengesanan jatuh masih menggunakan Sistem Personal
Emergency Response (PERS) yang tidak berfungsi secara automatik apabila jatuh dikesan.
Isu ini akan menyebabkan kecederaan teruk atau kematian apabila orang tua kehilangan
kesedaran atau pengsan selepas jatuh. Manakala dalam projek ini, masalah tersebut akan
diselesaikan dengan menggunakan sistem pengesanan jatuh automatik dengan
menggunakan sensor gerakan. Objektif projek ini adalah untuk mereka bentuk, membina
dan menganalisis sistem dengan menggunakan sensor pecutan tiga paksi dan juga
membina satu sistem pengesanan jatuh mudah alih yang mampu membezakan antara jatuh
dan aktiviti biasa (ADL) secara automatik. Perkakasan bagi sistem pengesanan jatuh
dibahagikan kepada dua bahagian. Bahagian pertama adalah untuk memperolehi data
untuk membezakan pecutan pelbagai jenis aktiviti manakala bahagian yang lain adalah
untuk sistem amaran kejatuhan yang mampu untuk menghantar SMS apabila kejatuhan
dikesan. Untuk mendapatkan ciri-ciri jatuh, data kejatuhan dan data ADL diperolehi
daripada penguji akan dianalisis. Hasilnya akan dikumpul daripada isyarat yang berbeza
yang dihasilkan oleh sensor pecutan. Daripada eksperimen yang telah dijalankan, ia
menunjukkan bahawa sistem pengesanan jatuh boleh mencapai ketepatan yang tinggi iaitu
96.11%. Dengan itu, kita boleh membuat kesimpulan bahawa sistem pengesanan jatuh
yang berjenis automatik akan menjadi peranti sokongan yang baik untuk warga tua. Jadi,
warga tua boleh mendapatkan bantuan perubatan secara tidak langsung apabila jatuh
berlaku.
ix
TABLE OF CONTENT
CHAPTER
CONTENTS
PAGE
PROJECT TITLE .....................................................................................i
DECLARATION STATUS OF REPORT FORM ............................... ii
DECLARATION .................................................................................... iii
SUPERVISOR DECLARATION ..........................................................iv
DEDICATION .......................................................................................... v
ACKNOWLEDGEMENT ......................................................................vi
ABSTRACT ........................................................................................... vii
ABSTRAK ............................................................................................ viii
TABLE OF CONTENT ..........................................................................ix
LIST OF TABLES ............................................................................... xiii
LIST OF FIGURES ..............................................................................xiv
LIST OF APPENDIX ...........................................................................xvi
1
INTRODUCTION .................................................................................... 1
BACKGROUND STUDY ................................................................ 1
PROBLEM STATEMENT ............................................................... 3
OBJECTIVE ..................................................................................... 4
x
SCOPE OF WORK ........................................................................... 4
METHODOLOGY............................................................................ 5
2
LITERATURE REVIEW ........................................................................ 7
CHARACTERISTIC OF FALL ....................................................... 7
2.1.1 Falls from Walking or Standing On the Floor .................... 7
2.1.2 Falls from Sleep (on bed) ................................................... 8
2.1.3 Falls from Sitting (on chair) ............................................... 8
CLASSIFICATION OF FALL DETECTION SYSTEM ................. 9
2.2.1 Context-aware System ...................................................... 10
2.2.1.1 Context-aware System .......................................... 10
2.2.1.2 Ambience Devices Approaches ............................ 13
2.2.2 Pervasive Devices Approaches......................................... 14
2.2.3 Wearable Devices Approaches ......................................... 15
3
2.3
Comparison of different fall detection system ............................. 22
2.4
Analysis for Acceleration Data ..................................................... 22
2.5
Advantages of the Wearable Fall Detection System .................... 24
METHODOLOGY ................................................................................. 25
APPROACHES OF DESIGN ......................................................... 25
FOR ANALYSIS STAGE (DATA ACQUISITION) ..................... 26
3.2.1 Tri-axialAccelerometer..................................................... 27
xi
3.2.2 Microcontroller (Arduino) ................................................ 27
3.2.3 Xbee Transceiver .............................................................. 29
FOR NOTIFICATION STAGE (SOFTWARE PART) ................. 31
3.3.1 GSM module .................................................................... 32
3.3.2 Mobile phone .................................................................... 33
4
RESULT AND DISCUSSION ............................................................... 34
CONNECTION OF FALL DETECTION SYSTEM ..................... 34
POSITION OF FALL DETECTION DEVICES ON BODY ......... 38
APPROACHES FOR DATA ACQUISITION ............................... 39
ACCELERATION DATA .............................................................. 41
4.4.1 Activity 1: Walking .......................................................... 42
4.4.2 Activity 2: Sitting down ................................................... 43
4.4.3 Activity 3: Standing up ..................................................... 44
4.4.4 Activity 4: Walking up-stairs ........................................... 45
4.4.5 Activity 5: Walking down-stairs ...................................... 46
4.4.6 Activity 6: Falling ............................................................ 47
EVALUATION THE CAPABILITY OF THE FALL
DETECTION AND NOTIFICATION SYSTEM........................... 48
4.5.1 Metrics of Performance .................................................... 48
4.5.2 Experiment Result ............................................................ 49
4.5.3 Discussion on Devices Performance ................................ 50
xii
5
CONCLUSION AND RECOMMENDATION ................................... 52
REFERENCES ....................................................................................... 53
APPENDIX A ........................................................................................ 56
APPENDIX B ........................................................................................ 58
APPENDIX C ........................................................................................ 61
APPENDIX D ........................................................................................ 62
xiii
LIST OF TABLES
NO
TITLE
PAGE
2.1
Comparison of fall detection system
22
4.1
The ability of fall detection device
49
4.2
Comparison the ability of different method to detect fall
49
xiv
LIST OF FIGURES
NO
TITLE
PAGE
1.1
Graph of unintentional fall death rate [4]
2
1.2
Fall detection from “Mobile Help”
4
1.3
Methodology of project
5
2.1
Type of fall detection approaches and its hierarchy for the elderly
9
2.2
Framework of the fall detection and notification [12]
10
2.3
Photo of 3D head motion analysis [16]
11
2.4
Photo of Body shape change analysis [17]
13
2.5
Smartphone OS world market in year 2014 [21]
15
2.6
Block diagram of fall detection system structure [22]
16
2.7
Image of the fall detection terminal [22]
17
2.8
Placement of fall detection device [22]
17
2.9
Prototype of TelosW mote and its component layout [23]
19
2.10
Fall detection system architecture [23]
19
2.11
Block diagram of fall detection system [24]
21
2.12
Placement of fall detection device [24]
21
2.13
Component assembly [24]
21
2.14
Acceleration of fall from standing [22]
23
2.15
Acceleration noted for different activity [22]
24
3.1
Overall flow of fall detection system
26
3.2
The Flow of Analysis Stage
26
3.3
Triple Axis Accelerometer Breakout - ADXL345 [25]
27
3.4
The operation of microcontroller (Arduino board)
28
3.5
Arduino Mega 2560 R3-Main Board [26]
28
xv
NO
TITLE
PAGE
3.6
The operation of Xbee transmitter
29
3.7
The operation of Xbee receiver
30
3.8
Xbee 1mW wire Antenna- Series 1 module [27]
30
3.9
Xbee starter kit [28]
31
3.10
The flow of notification stage
31
3.11
The flow of operation of GSM
32
3.12
GSM shield [29]
33
4.1
Device for data analysis and fall detection
34
4.2
Circuit diagram of devices 1
35
4.3
Circuit diagram of devices 2
36
4.4
Prototype of devices 1
37
4.5
Prototype of devices 2
37
4.6
Placement of device on volunteer for data acquisition
38
4.7
Placement of device on volunteer for fall detection
38
4.8
Method used for data acquisition
39
4.9
Accelerometer reading via serial monitor
40
4.10
Real time acceleration data in Matlab
40
4.11
Graph of acceleration with different kind of activities
41
4.12
Young subject walking
42
4.13
Volunteer sitting down
43
4.14
Volunteer standing up
44
4.15
Tester walking up-stairs
45
4.16
Tester walking down-stair
46
4.17
Young subject falling
47
xvi
LIST OF APPENDICES
NO
TITLE
PAGE
A
CODING FOR SENDING X, Y AND Z VALUE
OF ADXL345 IN MICROCONTROLLER
56
B
CODING FOR RECEIVING X, Y AND Z VALUE OF
ADXL345 IN MATLAB
58
C
CODING TO GENERATE GRAPG OF ACCELERATION
IN MATLAB
61
D
SPECIFICATION OF ADXL345
62
1
CHAPTER 1
INTRODUCTION
This chapter discussed about background study, problem statement, objectives,
scope of work and also the methodology of the project.
Background Study
Fall is one of the significant health risk for the elderly population with the rise in
disability, morbidity, mortality and also frailty [1]. It is about 28-35% of the old citizen
aged 65 and above fall annually expand to 32-42% for those above 70 years old according
to World Health Organization [2]. The number of falls rises with frailty level and age.
Actually, a high occurrence of falls and fall involved injuries in the elderly population is
due to fall exponentially grow up with age-involved biological shifts. The injuries number
due to fall is estimated to be double in year 2030. In this situation, supporting devices
which can help to mitigate this kind of health issue are a community demand [3].
2
Figure 1.1: Graph of unintentional fall death rate [4]
In addition, the benefit of fall detector is that it can minimize the time the elderly
remain lying on the ground after falling known as “long lie”. The important factor that
define the severity of the fall is within this period of time. Most of the ageing people are
unable to stand up again if there is not support has given and the continuous long lie can
cause dehydration, hypothermia, pressure sores and bronchopneumonia [3]. This case is
even worse if the elderly lives with their own or loss of consciousness after experience a
fall. Fall detector is still not a popular devices used and do not see any significant impact
on the old people yet. Most of the latent users do not be aware of this system but once the
concept is delivered, they realize that it was such a helpful devices to alter their safety and
security in house [3].
Due to this and others reason, the number of research for fall detection system has
expanded significantly over these few years. Unluckily, there are merely a small portion
of people reviews on fall detection. In year 1998, the first automatic fall detector has been
introduced by Williams which is operate by using piezoelectric sensor to sense the impact
shock [5]. In addition, it also included mercury lean switch to sense the position of user’s
body. Later on, this project was continued by Doughty and then a firm named “Tunstall”
has manufactured a commercial vision foundation for this system [6]. There is about
ninety percent of accuracy has been declared by that system but that system had been
3
tested by a wooden sample instead of testing by the old citizen. Besides that, Kangas
assessed 3 distinct algorithms of fall detection by using 3-axis accelerometer attached to
the head, waist and wrist. After testing fall detection with the approaches of using 3-axis
accelerometer attached to the head or waist, the result showed that a threshold- based
algorithm can reach high specificity and sensitivity which is above 90% for both [7].
Majority of fall detection application were still in the form of prototype for
principle study, so a wearable fall detection devices will be slightly bigger in size. Thus,
this project aims to develop a portable wearable wireless fall detection system with a
satisfy accuracy to distinguish between activity of daily life (ADL) and fall so that the
user can get the help in shortest times.
Problem statement
Accidents such as falling can be a serious problem for the elderly. Increasing
worldwide elderly population could augment serious effects and problems caused by fall.
Detection of a fall would help to reduce the time between the fall and the arrival of medical
attention [8]. It could be mortal if the victim unable to seek help within 3 days (72 hours).
For the old citizen who live alone in the rural area, if the accident is happened then others
might not be known thus increasing the mortality rate. So a fall detection system equipped
with notification features is developed in order to offer effective medical help to falling
sufferer.
Nowadays, most of the fall detection system available on the market is still operate
through a manually Personal Emergency Response System (PERS). It others word, if there
is a fall happened to an old people then they have to operate the falling system by
themselves. For example, if they wear the push-button pendant then they have to push the
button after they fall down accidentally. One of the fall detection devices form “Mobile
Help” company which is unable to send an alert notification automatically when fall
occurred [9]. This situation arise a problem when the user has been loss of consciousness
or faint after a fall [10]. So they unable to send a notification or alert to let others to help
them.
4
Figure 1.2: Fall detection from “Mobile Help”
Although another fall detection system can operate automatically, but the wearable
devices still got the limited distance from the base unit. Once the user outside the house,
the devices may not able to notify the command center when fall is happened. For
instances, Alert-1 medical alert system will merely detect fall using sensor then sending
signal to base unit within the range of around 120 meter [11]. Just imagine the
consequences if the elderly who worn the devices which is goes out of the range of
operation? Surely, this situation will cause the whole system to malfunction. Thus, based
on the existed problem, the solution has been came out which refer to the objective stated
in section 1.3.
Objective
1.) To design the automatic fall detection system using tri-axial accelerometer sensor.
2.) To analyze the orientation or acceleration movement by collecting the pattern of
waveform for normal activities and fall.
Scope of work
This project will focused to the hardware part which including the Arduino,
Zigbee (xbee), tri-axial accelerometer, Global System for Mobile(GSM) module, a
computer and others which contains the functions of data acquisition and processing for
fall detection and notification. For software part, the analysis work or data acquisition by
assessing orientation, height or accelerated movement will be carry out using Matlab
software. The prototype can only operate in a range of less than 100m (Outdoor/RF lineof-sight range) and within 30m (Indoor/Urban range) from the base unit by using the Xbee
5
wireless devices when doing an analysis to design algorithm by collecting pattern of
waveform. However, the wearable device can be used in the place where mobile network
coverage is available because it is operate using GSM technology. In short, this project
will include the developed of hardware part and also collect the pattern of waveform of
fall and activity of daily life (ADL) for analysis section.
Methodology
Figure 1.3: Methodology of project
6
First of all, the project will start by design a fall detection system. The designing
of the circuit will focus on the device that has been wear by the elderly to detect the fall.
The main equipment of the wearable devices has including the Arduino, Xbee, GSM and
tri-axial accelerometer.
After completing the circuit simulation then construction of the circuit should be
carry out. At the beginning, the circuit should be construct on the bread board to prevent
any broken of the component or equipment when connecting the circuit. The generally
idea of the fall detection system is by using the tri-axial accelerometer that has been
connect with the Arduino and GSM to detect the fall among the elderly citizen. It is a
wearable wireless device that can detect the fall automatically and send the signal to the
base unit (Computer) through Xbee for data acquisition (collect pattern of waveform).
There are two antenna module of Xbee: Transmitter module locate at the wearable device
while another is connected to the computer. Then microcontroller which contain the
algorithm to differentiate between ADL and fall will send the message to the person that
desired to inform through GSM when a fall occurred.
When the prototype has been successfully built, then analyze the orientation,
height or accelerated movement in order to differentiate the normal activity and fall. After
the result is obtained then it mean that the project is successfully complete.
7
CHAPTER 2
LITERATURE REVIEW
2
In this chapter, characteristics of fall, classification of fall detection system,
comparison of different fall detection system and also analysis for acceleration data based
on the previous researches has been discussed.
Characteristic of Fall
Specifying the features of fall is one of the vital endeavor because it will easier for
us not merely in understand the algorithm that already exist but also lead us the way to
create a new algorithms design. This is definitely real since an algorithms must be build
based on some features of fall. There are typically consists of four types of fall based on
the scenario of fall happened such as fall due to walking or standing on the floor; fall due
to sleep (on bed); fall due to sitting (on chair); fall due to standing on assist like a ladder
(normally a tool). The last type of fall are normally not much related to the elderly but
more associated to people who are working. So majority of the old citizen are threatened
by the first 3 category of falls. Although the first 3 category having some similar
characteristics but they having significant distinct characteristic [12].
2.1.1
Falls from Walking or Standing On the Floor
The characteristic noticed are shown below:
1.
Time taken of the fall is about 1-2 seconds, constituted of some sub-actions.
2.
The person is at standing position at beginning of the fall.
3.
In the end of fall process, the head will lies on the floor.
8
4.
There is only one direction of the fall experienced by the person. As a
conclusion, both the center of weight and the head of the person shift roughly
in one level when falling.
5.
Due to standing height to the lying stage, the head height reduced. The head
is considered as free falling.
6.
The head position is within a circle with centered at the foot position from the
last standing period and with the radius of the person height.
2.1.2
Falls from Sleep (on bed)
The characteristic noticed are shown below:
1.
Time taken of the fall is about 1-3 seconds, constituted of some sub-actions.
2.
The person is at lying position at beginning of the fall.
3.
Due to bed height to the lying stage of the person, the body height reduced.
The body is considered as free falling.
4.
2.1.3
The person body lying on the floor is near to bed.
Falls from Sitting (on chair)
The characteristic noticed are shown below:
1.
Time taken of the fall is about 1-3 seconds, constituted of some sub-actions.
2.
The person is at sitting position at beginning of the fall.
3.
Due to sit height to the lying stage of the person, the head height reduced. The
head is considered as free falling.
DESIGN OF MOTION DETECTION USING TRI-AXIAL
ACCELEROMETER FOR THE ELDERLY
LIM HOK HONG
This Report Is Submitted In Partial Fulfillment of Requirement For The
Bachelor Degree of Electronic Engineering (Industrial Electronic)
Fakulti Kejuruteraan Elektronik dan Kejuruteran Komputer
Universiti Teknikal Malaysia Melaka
JUNE 2016
ii
DECLARATION
UNIVERSTI TEKNIKAL MALAYSIA MELAKA
FAKULTI KEJURUTERAAN ELEKTRONIK DAN KEJURUTERAAN KOMPUTER
BORANG PENGESAHAN STATUS LAPORAN
PROJEK SARJANA MUDA II
Tajuk Projek
:
DESIGN OF MOTION DETECTION USING TRI-AXIAL ACCELEROMETER FOR
THE ELDERLY
Sesi Pengajian
:
1
5
/
1
6
Saya LIM HOK HONG mengaku membenarkan Laporan Projek Sarjana Muda ini disimpan di Perpustakaan
dengan syarat-syarat kegunaan seperti berikut:
1. Laporan adalah hakmilik Universiti Teknikal Malaysia Melaka.
2. Perpustakaan dibenarkan membuat salinan untuk tujuan pengajian sahaja.
3. Perpustakaan dibenarkan membuat salinan laporan ini sebagai bahan pertukaran antara institusi
pengajian tinggi.
4. Sila tandakan ( √ ) :
SULIT*
TERHAD**
*(Mengandungi maklumat yang berdarjah keselamatan atau
kepentingan Malaysia seperti yang termaktub di dalam AKTA
RAHSIA RASMI 1972)
**(Mengandungi maklumat terhad yang telah ditentukan oleh
organisasi/badan di mana penyelidikan dijalankan)
TIDAK TERHAD
Disahkan oleh:
__
_________________________
(TANDATANGAN PENULIS)
Tarikh: ………………………..
___________________________________
(COP DAN TANDATANGAN PENYELIA)
Tarikh: ………………………..
iii
DEDICATION
“I hereby declare that the work in this project is my own except for summaries and
quotations which have been duly acknowledge.”
Signature
: ....................................................
Author
: LIM HOK HONG
Date
: 15TH JUNE 2016
iv
“I acknowledge that I have read this report and in my opinion this report is sufficient in
term of scope and quality for the award of Bachelor of Electronic Engineering
(Industrial Electronics) with Honours.”
Signature
: .........................................................
Supervisor’s Name
: DR. ANIS SUHAILA BINTI MOHD ZAIN
Date
: 15TH JUNE 2016
v
A very special dedication for my beloved family especially to my parents, Lim Kui Seng
and Liong Siu Moi.
Also for my gracious supervisor Dr. Anis Suhaila Binti Mohd Zain.
vi
ACKNOWLEDGEMENT
Thank God for the blessings and grace - I could complete this meaningful final
year project for two semester at Universiti Teknikal Malaysia Melaka (UTeM) and to
complete this FYP thesis. Special thanks also given to Faculty of Electronic and Computer
Engineering (FKEKK) for give us chances to gain a lot of useful knowledge and skill.
Besides that, faculty also provides many facilities for us when doing our project.
Apart from that, the person that I would like to express the deepest appreciation is
my project supervisor, Dr. Anis Suhaila Binti Mohd Zain. I am the lucky person who
supervised under Dr Anis because madam is a very caring and brilliant guidance that
always lead us to a solution. She had gave me a hand when I am facing different kind of
difficulties and challenging.
In addition, I would like to thank my friends and family who always help
me in term of mentally or physically support. Without them, sure I am not able to
accomplish my project with flying colors within this two semester. Last but not least, I
would like to offer my regards and blessings to all of those who supported me in any
respect during the completion of my final year project.
vii
ABSTRACT
Falls are a major public health problem among older people nowadays. Most of
the fall detection device is still using the Personal Emergency Response System (PERS)
which is not function automatically when a fall is detected. This issue will cause severe
injuries or even death when the elderly loss of consciousness or faint after a fall. But in
my project, this problem has been solved by using the automatically fall detection system
by using motion sensor. The goal of this project is to design, construct and analyze the
system by using the tri-axial accelerometer sensor and to develop a portable fall detection
system, which is capable of automatically discriminating between falls and Activities of
Daily Life (ADL). The hardware part of fall detection system is divided into two part.
First part is for data acquisition in order to differentiate the acceleration of different kind
of activity while another part is for the fall alert system which is capable to sending the
SMS when a fall is detected. To extract the features of falls, falls data and ADL data
obtained from young subjects are analyzed. The result will be collect from the different
signal that produced by the accelerometer. From the experiment that has been carried out,
it shows that the fall detection system can accomplished an accuracy of 96.11%. It can be
concluded that, the automatically fall detection system will being a good supporting
device for the elderly. So, the elderly citizen can get the medical help when the
accidentally fall is happened.
viii
ABSTRAK
Jatuh adalah masalah kesihatan awam terutamanya di kalangan orang yang lebih
tua. Kebanyakan peranti pengesanan jatuh masih menggunakan Sistem Personal
Emergency Response (PERS) yang tidak berfungsi secara automatik apabila jatuh dikesan.
Isu ini akan menyebabkan kecederaan teruk atau kematian apabila orang tua kehilangan
kesedaran atau pengsan selepas jatuh. Manakala dalam projek ini, masalah tersebut akan
diselesaikan dengan menggunakan sistem pengesanan jatuh automatik dengan
menggunakan sensor gerakan. Objektif projek ini adalah untuk mereka bentuk, membina
dan menganalisis sistem dengan menggunakan sensor pecutan tiga paksi dan juga
membina satu sistem pengesanan jatuh mudah alih yang mampu membezakan antara jatuh
dan aktiviti biasa (ADL) secara automatik. Perkakasan bagi sistem pengesanan jatuh
dibahagikan kepada dua bahagian. Bahagian pertama adalah untuk memperolehi data
untuk membezakan pecutan pelbagai jenis aktiviti manakala bahagian yang lain adalah
untuk sistem amaran kejatuhan yang mampu untuk menghantar SMS apabila kejatuhan
dikesan. Untuk mendapatkan ciri-ciri jatuh, data kejatuhan dan data ADL diperolehi
daripada penguji akan dianalisis. Hasilnya akan dikumpul daripada isyarat yang berbeza
yang dihasilkan oleh sensor pecutan. Daripada eksperimen yang telah dijalankan, ia
menunjukkan bahawa sistem pengesanan jatuh boleh mencapai ketepatan yang tinggi iaitu
96.11%. Dengan itu, kita boleh membuat kesimpulan bahawa sistem pengesanan jatuh
yang berjenis automatik akan menjadi peranti sokongan yang baik untuk warga tua. Jadi,
warga tua boleh mendapatkan bantuan perubatan secara tidak langsung apabila jatuh
berlaku.
ix
TABLE OF CONTENT
CHAPTER
CONTENTS
PAGE
PROJECT TITLE .....................................................................................i
DECLARATION STATUS OF REPORT FORM ............................... ii
DECLARATION .................................................................................... iii
SUPERVISOR DECLARATION ..........................................................iv
DEDICATION .......................................................................................... v
ACKNOWLEDGEMENT ......................................................................vi
ABSTRACT ........................................................................................... vii
ABSTRAK ............................................................................................ viii
TABLE OF CONTENT ..........................................................................ix
LIST OF TABLES ............................................................................... xiii
LIST OF FIGURES ..............................................................................xiv
LIST OF APPENDIX ...........................................................................xvi
1
INTRODUCTION .................................................................................... 1
BACKGROUND STUDY ................................................................ 1
PROBLEM STATEMENT ............................................................... 3
OBJECTIVE ..................................................................................... 4
x
SCOPE OF WORK ........................................................................... 4
METHODOLOGY............................................................................ 5
2
LITERATURE REVIEW ........................................................................ 7
CHARACTERISTIC OF FALL ....................................................... 7
2.1.1 Falls from Walking or Standing On the Floor .................... 7
2.1.2 Falls from Sleep (on bed) ................................................... 8
2.1.3 Falls from Sitting (on chair) ............................................... 8
CLASSIFICATION OF FALL DETECTION SYSTEM ................. 9
2.2.1 Context-aware System ...................................................... 10
2.2.1.1 Context-aware System .......................................... 10
2.2.1.2 Ambience Devices Approaches ............................ 13
2.2.2 Pervasive Devices Approaches......................................... 14
2.2.3 Wearable Devices Approaches ......................................... 15
3
2.3
Comparison of different fall detection system ............................. 22
2.4
Analysis for Acceleration Data ..................................................... 22
2.5
Advantages of the Wearable Fall Detection System .................... 24
METHODOLOGY ................................................................................. 25
APPROACHES OF DESIGN ......................................................... 25
FOR ANALYSIS STAGE (DATA ACQUISITION) ..................... 26
3.2.1 Tri-axialAccelerometer..................................................... 27
xi
3.2.2 Microcontroller (Arduino) ................................................ 27
3.2.3 Xbee Transceiver .............................................................. 29
FOR NOTIFICATION STAGE (SOFTWARE PART) ................. 31
3.3.1 GSM module .................................................................... 32
3.3.2 Mobile phone .................................................................... 33
4
RESULT AND DISCUSSION ............................................................... 34
CONNECTION OF FALL DETECTION SYSTEM ..................... 34
POSITION OF FALL DETECTION DEVICES ON BODY ......... 38
APPROACHES FOR DATA ACQUISITION ............................... 39
ACCELERATION DATA .............................................................. 41
4.4.1 Activity 1: Walking .......................................................... 42
4.4.2 Activity 2: Sitting down ................................................... 43
4.4.3 Activity 3: Standing up ..................................................... 44
4.4.4 Activity 4: Walking up-stairs ........................................... 45
4.4.5 Activity 5: Walking down-stairs ...................................... 46
4.4.6 Activity 6: Falling ............................................................ 47
EVALUATION THE CAPABILITY OF THE FALL
DETECTION AND NOTIFICATION SYSTEM........................... 48
4.5.1 Metrics of Performance .................................................... 48
4.5.2 Experiment Result ............................................................ 49
4.5.3 Discussion on Devices Performance ................................ 50
xii
5
CONCLUSION AND RECOMMENDATION ................................... 52
REFERENCES ....................................................................................... 53
APPENDIX A ........................................................................................ 56
APPENDIX B ........................................................................................ 58
APPENDIX C ........................................................................................ 61
APPENDIX D ........................................................................................ 62
xiii
LIST OF TABLES
NO
TITLE
PAGE
2.1
Comparison of fall detection system
22
4.1
The ability of fall detection device
49
4.2
Comparison the ability of different method to detect fall
49
xiv
LIST OF FIGURES
NO
TITLE
PAGE
1.1
Graph of unintentional fall death rate [4]
2
1.2
Fall detection from “Mobile Help”
4
1.3
Methodology of project
5
2.1
Type of fall detection approaches and its hierarchy for the elderly
9
2.2
Framework of the fall detection and notification [12]
10
2.3
Photo of 3D head motion analysis [16]
11
2.4
Photo of Body shape change analysis [17]
13
2.5
Smartphone OS world market in year 2014 [21]
15
2.6
Block diagram of fall detection system structure [22]
16
2.7
Image of the fall detection terminal [22]
17
2.8
Placement of fall detection device [22]
17
2.9
Prototype of TelosW mote and its component layout [23]
19
2.10
Fall detection system architecture [23]
19
2.11
Block diagram of fall detection system [24]
21
2.12
Placement of fall detection device [24]
21
2.13
Component assembly [24]
21
2.14
Acceleration of fall from standing [22]
23
2.15
Acceleration noted for different activity [22]
24
3.1
Overall flow of fall detection system
26
3.2
The Flow of Analysis Stage
26
3.3
Triple Axis Accelerometer Breakout - ADXL345 [25]
27
3.4
The operation of microcontroller (Arduino board)
28
3.5
Arduino Mega 2560 R3-Main Board [26]
28
xv
NO
TITLE
PAGE
3.6
The operation of Xbee transmitter
29
3.7
The operation of Xbee receiver
30
3.8
Xbee 1mW wire Antenna- Series 1 module [27]
30
3.9
Xbee starter kit [28]
31
3.10
The flow of notification stage
31
3.11
The flow of operation of GSM
32
3.12
GSM shield [29]
33
4.1
Device for data analysis and fall detection
34
4.2
Circuit diagram of devices 1
35
4.3
Circuit diagram of devices 2
36
4.4
Prototype of devices 1
37
4.5
Prototype of devices 2
37
4.6
Placement of device on volunteer for data acquisition
38
4.7
Placement of device on volunteer for fall detection
38
4.8
Method used for data acquisition
39
4.9
Accelerometer reading via serial monitor
40
4.10
Real time acceleration data in Matlab
40
4.11
Graph of acceleration with different kind of activities
41
4.12
Young subject walking
42
4.13
Volunteer sitting down
43
4.14
Volunteer standing up
44
4.15
Tester walking up-stairs
45
4.16
Tester walking down-stair
46
4.17
Young subject falling
47
xvi
LIST OF APPENDICES
NO
TITLE
PAGE
A
CODING FOR SENDING X, Y AND Z VALUE
OF ADXL345 IN MICROCONTROLLER
56
B
CODING FOR RECEIVING X, Y AND Z VALUE OF
ADXL345 IN MATLAB
58
C
CODING TO GENERATE GRAPG OF ACCELERATION
IN MATLAB
61
D
SPECIFICATION OF ADXL345
62
1
CHAPTER 1
INTRODUCTION
This chapter discussed about background study, problem statement, objectives,
scope of work and also the methodology of the project.
Background Study
Fall is one of the significant health risk for the elderly population with the rise in
disability, morbidity, mortality and also frailty [1]. It is about 28-35% of the old citizen
aged 65 and above fall annually expand to 32-42% for those above 70 years old according
to World Health Organization [2]. The number of falls rises with frailty level and age.
Actually, a high occurrence of falls and fall involved injuries in the elderly population is
due to fall exponentially grow up with age-involved biological shifts. The injuries number
due to fall is estimated to be double in year 2030. In this situation, supporting devices
which can help to mitigate this kind of health issue are a community demand [3].
2
Figure 1.1: Graph of unintentional fall death rate [4]
In addition, the benefit of fall detector is that it can minimize the time the elderly
remain lying on the ground after falling known as “long lie”. The important factor that
define the severity of the fall is within this period of time. Most of the ageing people are
unable to stand up again if there is not support has given and the continuous long lie can
cause dehydration, hypothermia, pressure sores and bronchopneumonia [3]. This case is
even worse if the elderly lives with their own or loss of consciousness after experience a
fall. Fall detector is still not a popular devices used and do not see any significant impact
on the old people yet. Most of the latent users do not be aware of this system but once the
concept is delivered, they realize that it was such a helpful devices to alter their safety and
security in house [3].
Due to this and others reason, the number of research for fall detection system has
expanded significantly over these few years. Unluckily, there are merely a small portion
of people reviews on fall detection. In year 1998, the first automatic fall detector has been
introduced by Williams which is operate by using piezoelectric sensor to sense the impact
shock [5]. In addition, it also included mercury lean switch to sense the position of user’s
body. Later on, this project was continued by Doughty and then a firm named “Tunstall”
has manufactured a commercial vision foundation for this system [6]. There is about
ninety percent of accuracy has been declared by that system but that system had been
3
tested by a wooden sample instead of testing by the old citizen. Besides that, Kangas
assessed 3 distinct algorithms of fall detection by using 3-axis accelerometer attached to
the head, waist and wrist. After testing fall detection with the approaches of using 3-axis
accelerometer attached to the head or waist, the result showed that a threshold- based
algorithm can reach high specificity and sensitivity which is above 90% for both [7].
Majority of fall detection application were still in the form of prototype for
principle study, so a wearable fall detection devices will be slightly bigger in size. Thus,
this project aims to develop a portable wearable wireless fall detection system with a
satisfy accuracy to distinguish between activity of daily life (ADL) and fall so that the
user can get the help in shortest times.
Problem statement
Accidents such as falling can be a serious problem for the elderly. Increasing
worldwide elderly population could augment serious effects and problems caused by fall.
Detection of a fall would help to reduce the time between the fall and the arrival of medical
attention [8]. It could be mortal if the victim unable to seek help within 3 days (72 hours).
For the old citizen who live alone in the rural area, if the accident is happened then others
might not be known thus increasing the mortality rate. So a fall detection system equipped
with notification features is developed in order to offer effective medical help to falling
sufferer.
Nowadays, most of the fall detection system available on the market is still operate
through a manually Personal Emergency Response System (PERS). It others word, if there
is a fall happened to an old people then they have to operate the falling system by
themselves. For example, if they wear the push-button pendant then they have to push the
button after they fall down accidentally. One of the fall detection devices form “Mobile
Help” company which is unable to send an alert notification automatically when fall
occurred [9]. This situation arise a problem when the user has been loss of consciousness
or faint after a fall [10]. So they unable to send a notification or alert to let others to help
them.
4
Figure 1.2: Fall detection from “Mobile Help”
Although another fall detection system can operate automatically, but the wearable
devices still got the limited distance from the base unit. Once the user outside the house,
the devices may not able to notify the command center when fall is happened. For
instances, Alert-1 medical alert system will merely detect fall using sensor then sending
signal to base unit within the range of around 120 meter [11]. Just imagine the
consequences if the elderly who worn the devices which is goes out of the range of
operation? Surely, this situation will cause the whole system to malfunction. Thus, based
on the existed problem, the solution has been came out which refer to the objective stated
in section 1.3.
Objective
1.) To design the automatic fall detection system using tri-axial accelerometer sensor.
2.) To analyze the orientation or acceleration movement by collecting the pattern of
waveform for normal activities and fall.
Scope of work
This project will focused to the hardware part which including the Arduino,
Zigbee (xbee), tri-axial accelerometer, Global System for Mobile(GSM) module, a
computer and others which contains the functions of data acquisition and processing for
fall detection and notification. For software part, the analysis work or data acquisition by
assessing orientation, height or accelerated movement will be carry out using Matlab
software. The prototype can only operate in a range of less than 100m (Outdoor/RF lineof-sight range) and within 30m (Indoor/Urban range) from the base unit by using the Xbee
5
wireless devices when doing an analysis to design algorithm by collecting pattern of
waveform. However, the wearable device can be used in the place where mobile network
coverage is available because it is operate using GSM technology. In short, this project
will include the developed of hardware part and also collect the pattern of waveform of
fall and activity of daily life (ADL) for analysis section.
Methodology
Figure 1.3: Methodology of project
6
First of all, the project will start by design a fall detection system. The designing
of the circuit will focus on the device that has been wear by the elderly to detect the fall.
The main equipment of the wearable devices has including the Arduino, Xbee, GSM and
tri-axial accelerometer.
After completing the circuit simulation then construction of the circuit should be
carry out. At the beginning, the circuit should be construct on the bread board to prevent
any broken of the component or equipment when connecting the circuit. The generally
idea of the fall detection system is by using the tri-axial accelerometer that has been
connect with the Arduino and GSM to detect the fall among the elderly citizen. It is a
wearable wireless device that can detect the fall automatically and send the signal to the
base unit (Computer) through Xbee for data acquisition (collect pattern of waveform).
There are two antenna module of Xbee: Transmitter module locate at the wearable device
while another is connected to the computer. Then microcontroller which contain the
algorithm to differentiate between ADL and fall will send the message to the person that
desired to inform through GSM when a fall occurred.
When the prototype has been successfully built, then analyze the orientation,
height or accelerated movement in order to differentiate the normal activity and fall. After
the result is obtained then it mean that the project is successfully complete.
7
CHAPTER 2
LITERATURE REVIEW
2
In this chapter, characteristics of fall, classification of fall detection system,
comparison of different fall detection system and also analysis for acceleration data based
on the previous researches has been discussed.
Characteristic of Fall
Specifying the features of fall is one of the vital endeavor because it will easier for
us not merely in understand the algorithm that already exist but also lead us the way to
create a new algorithms design. This is definitely real since an algorithms must be build
based on some features of fall. There are typically consists of four types of fall based on
the scenario of fall happened such as fall due to walking or standing on the floor; fall due
to sleep (on bed); fall due to sitting (on chair); fall due to standing on assist like a ladder
(normally a tool). The last type of fall are normally not much related to the elderly but
more associated to people who are working. So majority of the old citizen are threatened
by the first 3 category of falls. Although the first 3 category having some similar
characteristics but they having significant distinct characteristic [12].
2.1.1
Falls from Walking or Standing On the Floor
The characteristic noticed are shown below:
1.
Time taken of the fall is about 1-2 seconds, constituted of some sub-actions.
2.
The person is at standing position at beginning of the fall.
3.
In the end of fall process, the head will lies on the floor.
8
4.
There is only one direction of the fall experienced by the person. As a
conclusion, both the center of weight and the head of the person shift roughly
in one level when falling.
5.
Due to standing height to the lying stage, the head height reduced. The head
is considered as free falling.
6.
The head position is within a circle with centered at the foot position from the
last standing period and with the radius of the person height.
2.1.2
Falls from Sleep (on bed)
The characteristic noticed are shown below:
1.
Time taken of the fall is about 1-3 seconds, constituted of some sub-actions.
2.
The person is at lying position at beginning of the fall.
3.
Due to bed height to the lying stage of the person, the body height reduced.
The body is considered as free falling.
4.
2.1.3
The person body lying on the floor is near to bed.
Falls from Sitting (on chair)
The characteristic noticed are shown below:
1.
Time taken of the fall is about 1-3 seconds, constituted of some sub-actions.
2.
The person is at sitting position at beginning of the fall.
3.
Due to sit height to the lying stage of the person, the head height reduced. The
head is considered as free falling.