Smart glove hand gesture vocalizer final year report

SMART GLOVE: HAND GESTURE
VOCALIZER FOR DEAF AND MUTE
B.E. SENIOR DESIGN PROJECT REPORT
Electronics Specialization
Prepared By
Ali Ahmed Siddiqui 7782
Hafiz Ahsen Siddique 7453
Muhammad Noman Khan 7783
Project Advisor
HoD Electrical/Associate Professor
Dr. Sameer Qazi
College of Engineering
PAF – Karachi Institute of Economics and
Technology Karachi

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ACKNOWLEDGMENT
In the beginning, all praise to the almighty Allah, who provided us strength by which
we have completed this project, it was no doubt is the blessing from Allah. During the
project completion till the last day and the span this report was compiled and written, a
number of people give us immense support and encouragement.
We have applied lot of efforts to make this project. However, it will not be able to occur
without a support and help of many people. We would like to express our gratitude to
our advisor Dr. Sameer Qazi for the sincere guidance, motivation and constant
supervision as well as for giving necessary knowledge which help us in our design and
creates the foundation for this project.
Moreover, we are extremely thankful to our very gentle personalities Ms. Bushra and
Dr. Hussain Pervaz and SDP Committee Member for their kind hearted motivation
and encouragement which embraced and motivated us during accomplishment of this
project. Enormous appreciations for giving us such kind attention and precious time
Furthermore, bags full of appreciations to our class fellows who helped us out with their
engineering abilities and to our parents, and siblings for their profound love and
dedication for us throughout lives. All that we have capable of is the result of our
teachings by our parents and teachers
We would also like to extend our gratitude to Director College of Engineering Mr.
NajeebHaider and our Institute PAF-KIET for the continuous support throughout the
academic life. We would like to thanks ICT RnD for the financial support of this
project and choosing our project for funding which helps us in the completion of
admirable project.

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ABSTRACT
Communication is the only channel by which we can share our feelings, opinions and
conveys message to others. But a person with a disability like dumb and deaf, faces a
lot of a problem and difficulty while communicating with normal people. It is estimated
and observed by a survey that about more than 10 billion of population in the world are
deaf and dumb. Generally, deaf and dumb people uses sign language in order to
communication with others which is based on hand gestures and also on specific
motions. But disable person faces a lot of problem while communicating with others
because mostly normal person do not know about sign language and they do not
understood it. Due to this fact deaf and dumb people are not given many opportunities
in our societies in different walks of life. So, there is always a communication barrier
between deaf and dumb people with normal people. To remove this communication
barrier we proposed our project which is smart glove hand gesture vocalizer to help
deaf and dumb people. The smart glove has the capability to convert hand gestures into
certain sound. So, it means by using smart glove there will be no communication
difference between deaf and dumb people with normal people. It is also one of the main
aim and objective of our project to help peoples and serve humanity.
The smart glove consist of a leather glove on which flex sensor is placed on each
finger to detect the bending movement of the fingers .The Deaf and dumb people also
uses hand motion with the hand gestures. So to detect hand motion we use
accelerometer which is placed on the backside of the leather glove. In our project we
also have used a 10 bit AVR microcontroller which takes the reading from the all the
analog sensor in the ADC pins. The smart glove has also contain a speech synthesizer
which give the output in the form of sound by getting and evaluating real time values
by the sensors. All the output sound in our project, which we used are in English
language.
Our project is also a social initiative step to uplift the deaf and dumb community so
they can express themselves in better way and can avail different opportunities in their
life what they really deserves. By using smart glove, the communication problem will
be solved and better relationship and understanding will build between peoples.

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KEYWORDS
TX : Transmitter
RX : Receiver
ADC : Analog to Digital Convertor
DAC : Digital to Analog Convertor
SDA : Serial Data
SCL : Serial Clock
I/O : Input output
RISC : Reduced Instruction Set Computer
CPU : Central Processing Unit
SPI : Serial Peripheral Interface
MEMS : Micro Electro Mechanical Systems
ALU : Arithmetic Logic Unit

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LIST OF FIGURES
Fig 3-1: Process Flow of smart glove
Fig 3-2: Schematices-1
Fig 3-4: ADXL335 testing
Fig 3-5: ADXL335 real time plot on Matlab
Fig 3-6: Flex sensor testing
Fig 3-7: Flex sensor real time plot on Matlab
Fig 3-8: Mode testing simulation
Fig 3-9: Communication testing between AVR and Arduino
Fig 3-10: DF mp3 mini player testing
Fig 3-11: Front view PCB schematic of main board
Fig 3-12: Rear view PCB schematic of main board
Fig 3-13: Front and rear view PCB schematic of sensor channel
Fig 3-14: ATMEGA16 Microcontroller
Fig 3-15: ATMEGA16 Microcontroller pin out diagram
Fig 3-16: Flex sensor
Fig 3-17: ADXL335 Accelerometer
Fig 3-18: DF mp3 mini player
Fig 3-19: Arduino Nano
Fig 4-1: Gesture 1
Fig 4-2: Gesture 2

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Fig 4-3: Gesture 3
Fig 4-4: Gesture 4
Fig 4-5: Gesture 5
Fig 4-6: Gesture 6
Fig 4-7: Gesture 7
Fig 4-8: Gesture 8
Fig 4-9: Gesture 9

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LIST OF TABLES
Table 4-1: Gesture values of Flex sensor
Table 4-2: Gesture values of accelerometer
Table 5-1: Component Cost
Table 5-1: Dumb and Deaf population statistics
Table 5-2: Marketing Analysis

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TABLE OF CONTENTS
TABLE OF CONTENTS.................................................................................................vii
Motivation:.................................................................................................................. ix
Problem Discussion: .................................................................................................... ix
Aim: ........................................................................................................................... ix
Objective:..................................................................................................................... x
Gantt Chart.................................................................................................................. xi
1 INTRODUCTION..................................................................................................... 1
1.1 Project scope ..................................................................................................... 2
1.2 Key Objectives ..................................................................................................2
1.3 Functionality ..................................................................................................... 2
2 DESIGN OBJECTIVES, ISSUE AND THEIR ANALYSIS......................................... 4
2.1 Design objective:............................................................................................... 4
2.2 Issue:................................................................................................................ 5
2.3 Analysis:........................................................................................................... 5
3 DESIGN SPECIFICATION....................................................................................... 6
3.1 Literature review................................................................................................ 6
3.1.1 Image Processing Method: .......................................................................... 6
3.1.2 Edge detection............................................................................................ 7
3.1.3 Signal to Noise Ratio .................................................................................. 8
3.2 Sensor and Microcontroller based Method:.......................................................... 8
3.3 Process Flow:.................................................................................................. 11
3.4 Schematics:..................................................................................................... 14
3.5 Algorithm:....................................................................................................... 15
3.6 Simulation:...................................................................................................... 18
3.6.1 Real-time interfacing of sensors................................................................. 18
3.7 PCB design: .................................................................................................... 22
3.8 Mechanical design: .......................................................................................... 23
3.9 Hardware and software:.................................................................................... 23
3.9.1 AVR microcontroller:............................................................................... 23
3.9.2 Flex sensor:.............................................................................................. 24
3.9.3 Accelerometer:......................................................................................... 25
3.9.4 DF mini mp3 player:................................................................................. 25
3.9.5 Arduino nano: .......................................................................................... 26

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3.9.6 Proteus:.................................................................................................... 26
3.9.7 Matlab:.................................................................................................... 27
3.9.8 EasyEDA:................................................................................................ 27
3.9.9 Atmel studio:............................................................................................ 27
3.10 Safety & precautions:....................................................................................... 28
4 TEST RESULTS AND THEIR ANALYSIS............................................................. 29
4.1 Analysis:......................................................................................................... 34
5 ECONOMIC ANALYSIS........................................................................................ 35
5.1 Prototype cost.................................................................................................. 35
5.2 Market analysis................................................................................................ 36
6 CONCLUSION....................................................................................................... 39
7 FUTURE RECOMMENDATION............................................................................ 40
8 REFERENCES....................................................................................................... 42

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PROJECT OBJECTIVES
Motivation:
Communication is the only way and procedure to express feeling, idea or conveys the
message to others. But a person with a disability like deaf and dumb faces difficulty in
order to communicate with the normal people. So there is always a communication
barrier between deaf and dumb people with normal people .Because of the disability
these people can not express themselves in a proper way to others. They get less
opportunities in the society instead what they really deserve. So to minimize the
communication barrier we have made a economical standalone device which can give
voice to mute person. By using smart glove a disable person can also get a chance to
express themselves more and grow in the society. By using smart glove there will be
no communication obstacle between deaf and dumb people with the normal people.
Problem Discussion:
In the last few years, there has been exponential increase in the population of hearing
impaired and speech disable people due to oral disease, birth defects or accident. When
a disable person like deaf and dumb person speaks to a normal people than normal
people find difficulty to communicate with the dumb and deaf people .Disable person
like deaf and dumb faces many difficulties in their life. One of the main problem they
are facing is about academic and social needs. Due the their disability the deaf and
dumb person are ignored by the people and give less priority as compare to normal
person. One of the main reason why deaf and dumb people are being ignored is
communication. Because most of the normal people can not understand sign language.
Due to this there is always a communication barrier between deaf and dumb people
with the people. There is also a lack of awareness regarded to diversity among the dumb
and deaf people. Also there are very less jobs available for disable person.
Aim:
The main aim of this project is a make cost effective and efficient system which can
give voice to voiceless person. This project will remove the communication barrier
between the disable person with the normal person. With the help of smart glove the
deaf and dumb person life will be much easier and they will get a chance to express
themselves more and can get more opportunities in their life.

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Objective:
Sign language is the only method and way in the communication between the normal
person and dumb person, but most of normal people’s can not understand sign language
because they do not know anything about sign language. Due to this disable person find
very difficulty in their life. So the main objective of our project is to make a very cost
effective system which will help dumb and deaf people to communicate with other. So
our project, smart glove will translate different hand gestures into sounds and convey
the messages of disable person to others. So by using smart glove there will be no
communication barrier between the two communities and also this is our main objective
of making of this project to help people and serve humanity.
Methodology:
Our proposed project consist of mainly of two parts section.
1.Transmitter part section
2.Receiver part section
The stages in the transmitter sections are
a. Flex sensors
b. Accelerometer
c. AVR atmega16 microcontroller
The stages in the receiver sections are
a. Mp3-DF player
b. Speaker
Arduino nano (work as both RX,TX for communication between AVR CONTROLLER
and Mp3-DFplayer)

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Marketor Industry adaptability/applications:
Our project smart glove is totally an application based project which is very helpful for
the deaf and dumb people because it translates hand gesture into sound. So that the
disable person can easily communicate with the others.
Gantt Chart
In the phase 1 we do all the research work about the components which include sensors,
microcontrollers and sound module. In phase 2 we do the programming of our project
on Atmel Studio. In phase 3 we make the actual hardware of our project on PCB board.
In the last phase we complete the final report of our project.

CHAPTER 1 INTRODUCTION
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CHAPTER 1
1 INTRODUCTION
Languages are natural way of communication, but between normal people and a deaf
person gestures are the only way of communication. People find difficulty in
communicating with normal people which creates language barrier. So, we propose a
gadget to minimize this barrier which converts hand gestures into voice which can
understand by normal people.
In recent years many devices are designed for handicapped people but for deaf and
dumb people no such devices are designed. These peoples have sign language to
communicate with us and we find difficulty to understand it. Sign language is a skill to
convey message by different gestures of hand. People lost their ability to speak in any
accident or they are born mute, it is quite difficult for him to convey message. To
remove this problem we propose this project.
The gesture detection and recognition system generally have two of the following
methodologies that is vision-based and image processing techniques and the third one
we applied is sensors and glove in image processing, the gestures digest are captured
into images and starts analyzing images with different types of algorithm to recognize
the particular gesture meaning. The technique we use accelerometer and flux sensors
which detect the movement of hands. Therefore we use ATMega 16 for interfacing with
these sensors and output synthesizer devices.
By name it reveals its specification that a machine that converts gestures into speech.
Hand gesture vocalize is typically designed for handicapped people ,for deaf and dumb
to reduce the communication gap between these people so that they can also take part
in the development of society and it rises their self-esteem to cop up with modern fast
developing technology era. In this project, we use K-means clustering algorithm , this
algorithm increases the efficiency ,the errors which may occur is due to different size
of hands , different physiques .The aspects on we have to work collection of data ,
sensors accuracy and minimize the time to speak i.e less latency in communication and
obviously remote size.

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1.1 Projectscope
This project opens the gate for new opportunities for the handicapped people make
them motivated enhance their capabilities so that they can move with the speed of the
era. By this product deaf and dumb people will do their regular work in a very felicitous
way and fulfill their regular needs. The project is designed with efficient algorithm so
that it can work with different physiques and anatomy different people. This project is
concentrated to make communication easier and faster between handicapped people
and normal people. Our scope contains three main objectives
 Selection and understanding efficient algorithm
 Design and implementation of hardware along with program code
 The practical results with different gesture.
1.2 Key Objectives
 Selection of algorithm for efficiency
 Understanding and Implementation of selected algorithm
 Resolving Gestures Problem
 Efficiency of Sensors
 Data collection
 Minimize the time to speak
 Low cost
1.3 Functionality
There are many ways to design hand gestures vocalizer, some designs are based on
digital image processing but our main focus is to achieve efficiency and remoteness and
time to speak. In our design we use flex sensors which changes its resistance on
bending we use it with fixed resistance ,followed by voltage divider . The signal went
to the ADC of microcontroller then the program executes send the signal to send the
microcontroller using I2C protocol, the second microcontroller has send the signal to

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DF mini mp3 player ,which play the voices. The flex sensors is implemented on gloves,
which make it wearable electronics. We use clustering algorithm, it is grouping of
objects in sets in such a way that objects in the different groups are more different to
each other to than those in other groups. Main task of clustering algorithm is
exploratory, data mining and for data analysis in many different fields like machine
learning, image processing, bio informatics and computer graphics. It is the simplest
unsupervised learning algorithms.I2C communication protocol is serial, half-duplex
,two wired communication interface protocol. It is used to connect low-speed devices
such as A/D and D/A convertors, I/O interfaces, microcontrollers, EPROMS in
embedded systems. Data is send by bit by bit along a single line, one is SDA which is
data line and other SCA which is serial Clock line.

CHAPTER 2 DESIGN OBJECTIVES,ISSUE AND THEIR ANALYSIS
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CHAPTER 2
2 DESIGN OBJECTIVES, ISSUE AND THEIR
ANALYSIS
2.1 Designobjective:
The main objective of making of this project is to make a very efficient system which
can help mute and deaf people while they are communicating with the others. Because
generally sign language is not understand by large number of peoples due to this deaf
and dumb people find very difficulty in conveying their messages, feeling and
communicate with others. So in this project we are trying to make a system which can
convert different hand gestures into different messages in sound which we assign. All
the sound messages which we are using in our project are used in daily routine by the
people. So, by using smart glove there will be no communication between deaf and
dumb people with normal. In this project we are flex sensors. Flex sensor are placed on
each finger of the glove. The flex sensor plays a very important role in the detection of
the bending of the fingers. The output of the flex sensor is in the form of the difference
in the voltage that changes and varies according to the amount of sensor bends by the
finger. Along with flex sensor we are also using another sensor for the detection of the
hand motion which is accelerometer. Also the output of accelerometer is in the form
analog voltages which varies according to the amount of motion of the hand .The output
of the both the sensors which are flex sensor and accelerometer are given to ADC pins
of the controller. The microcontroller which we are using is of the AVR microcontroller
which ATMEGA16. After receiving the data from the sensors than the microcontroller
will send the signal to the voice mp3 module to play the sound of the certain gesture
which is just made by the user.

CHAPTER 2 DESIGN OBJECTIVES,ISSUE AND THEIR ANALYSIS
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2.2 Issue:
The main issue which we face while making this project is the difference in the anatomy
of people. Everyone has naturally different bone structure. The way of making the same
gesture is different from the other .so we have to deal with this problem and make the
output efficient in the hand of different people. We are also face a lot of problem in
the calibration of the sensor. Because if the sensor are not calibrated properly than the
output will not be efficient when more than one person wear this glove because the
output of the sensor deviates in different hand. To make our project result better we
have to collect sensors reading of different hand gestures in the hand of different
peoples. We also have to do a lot of research and select a sound a module in which we
can save the sound file in large amount because the available sound has only 8 channel
means it can only play 8 audio voices. We also have faces problem while
communication between the microcontroller and the mp3 module to play the sound for
the certain hand gesture which is just made by a person .
2.3 Analysis:
To overcome the problem of different anatomy of the people we implement a very
efficient algorithm so that the output result is very efficient and give the accurate output
in the different hand. In the algorithm we calibrated the output value of the sensor which
play very important role in our project. For the reading of the sensor we made our own
LCD design circuit to visualize and monitor the analog values of the sensor by making
different hand gesture .We also made our project very cost effective so that the every
disable person can easily purchase smart glove without facing any financial burden. So
we do a lot of research on the component which we use in our project and do not
compromise in the quality and selected thosecomponent which are cheap to make the
project cost effective and efficient. For programming purpose firstly, we use mapping
technique on the data which we collected from the different people but we difficulty
because there was some resolution issue appears. So then we programming the
microcontroller without mapping the data.

CHAPTER 3 DESIGN SPECIFICATIONS
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CHAPTER 3
3 DESIGN SPECIFICATIONS
3.1 Literature review
The language detection is generally based on two methods that is digital image
processing or vision based and the second one is microcontroller and sensors based
glove method. In the first methodology, we capture the different gestures into images
and by analyzing with different algorithms for image processing and we analyze it by
matching with respective key gestures frames with the help of predefined source.
3.1.1 Image Processing Method:
Now a days, in digital image processing linear processing techniques are used
extensively. Due to their simplicity in mathematics and unifying systems existence
make their implementation and design easy.
Moreover, this technique gives satisfactory performance for numerous complications.
But, somehow a numerous Digital image processing problems cannot be solved by this
algorithm. Linear filter are generally used for image filtering. They are not a efficient
for non linear formation of image model. Moreover humans can detect image edges
hand lines with high frequencies this human behavior has non linear characteristics.
Linear processing generally contains low pass characters. These filters destroy edges
and ending line. This is why, people move towards non linear image processing
techniques. The following are the main classes of non linear digital image processing
technique.
 Homomorphic Technique
 Mathematical morphology
 Order statistics filter technique
 Polynomial Filter Technique
 Neural Networks
 Non-linear image restoration

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One of the primary confinements of nonlinear methods is the absence of a binding
together hypothesis that can include all performing nonlinear channel classes. Every
nonlinear class preparing procedures has its very own scientific devices that can give
great examination of its execution. Cross treatment of these classes has been appeared
to guarantee. For instance, mathematical morphology and order statistics technique
have too, the majority of the detailed work has been connected to computerized picture
procedure. We will concentrate our introduction on computerized picture preparing
applications, with the end goal to render it more brief. In the accompanying, we will
center around the portrayal of the request insights systems. Albeit such strategies have
been utilized to different computerized digital image processing.
Major Problems occurs in Image Processing
3.1.2 Edge detection
Edge detection is generally known split-region method make a hypothetical edge
creation contain the information of image which is need to be processed. This is how
original image can be detected by its edge. The method for edge based edge detection
is object localization and border detection criteria using Adaptive Nuero-Fuzzy system.
This system provides characterization and non scale edge detection by providing
determined systematic threshold step. Image analysis on multi scale assessment is
designed for an efficient FPGA implementation of image filtering. Xilinx System
Generator is designed methodology for evaluation and implementation of digital
image processing algorithms on configuration of architecture using VHDL.

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3.1.3 Signal to Noise Ratio
Image which are with extracted logo after watermarking are assessed for signal to noise
ratio. Effect of different scaling parameters can be measured as signal to noise ratio
for output and extracted image logo.
There are many types of noises some are name below:
 Additive Noise
 Multiplicative noise
 Impulse noise
 Quantization Noise
 Aliasing
 The Noise Function
The main disadvantage of using this methodology is it requires more complex and
complicated algorithms and It also requires proper lighting and background. This
method is very difficult for mobility. The image processing requires a lot of processing
power. It also has some limitations
3.2 Sensorand MicrocontrollerbasedMethod:
Hence we use the second method so the most important thing is to select the cost
effective efficient and first microcontroller if we went to the microprocessor their cost
very high.
In the market the available microcontrollers are of AVR family and PIC family which
are very low cost architectures. In PIC microcontroller, 1 instruction took four cycles
to execute whereas AVR microcontrollers took only one cycle to execute. This shows
that AVR microcontroller is faster than PIC microcontroller. We need eight analogue
input in our project because we use five flex sensor on each finger to detect the bending
movement of the fingers and we also use an accelerometer sensor to detect the motion
of the hand in all three dimension. That's why we choose microcontroller of AVR
family which is AT Mega-16 microcontroller. It has eight ADC input pins and have a
16 Megahertz clock designed on advanced RISC architecture it is a high performance,
low-power Atmel 8 bit microcontroller 131 powerful instructions which are mostly
single cycle executed. It has up to 16 MIPS throughput at 16 Megahertz. It has 16k
bytes of in system self Programmable flash memory in 512 bytes EEPROM. The main

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peripheral features are two 8 bit counters and one 16 bit timer and counter, 4 PWM
channels and 8 Channel 10 bit ADC. AT Mega 16 microcontroller 32 Programmable
I/O lines and contains 40 pins, A JTAG interface, 3 flexible timers, counters with
compare modes, a serial Programmable USART, internal and external interrupts, a byte
oriented 2 wire serial interface, a Programmable Watchdog timer with internal
oscillator and SPI serial port and 6 software selectable power modes. The ADC noise
reduction mode stops the CPU and all IO modules except asynchronous timer and ADC,
to minimize switching noise during the ADC conversion. In standby mode the resonator
is turning while the rest of the device sleeping this allows very fast start up combined
with low power consumption. That's the reason we choose AVR Atmega16 controller
rather than 8051 or PIC controller .
The sensors which we used to detect the bending movement of finger is flex sensor.
Flex sensor is like a variable resistor whose resistance changes with amount of its
bending, which is the result of the movement of finger. The flex sensor is an analog
sensor. Flex sensor has average resistance of about 10k ohm resistance. On bending the
resistance offered is increased. Flex sensor is fabricated by direct ink write (DIW)
technique using carbon paste. Buy this technique we can deposit a variety of material
on different substrates. For fabrication of PCBS and electrodes for different devices,
this method is widely used .Taguchi methodology used for control and effective
patterning and printing. For flexible PET substrate generation, carbon paste is used as
ink. The sensors are fabricated through DIW technique and their electrical
characteristics were evaluated. Hence, this technique produces stable and reliable in
performance of the sensors.
To detect different rotations of hand we use accelerator module ADXL 335. It is a least
power device and has a capability to measures 3-Axis acceleration. It gives output in
the terms of analogue voltages corresponding to acceleration. It is a MEMS device
(Micro-Electro-Mechanical Systems). These chip based Technology made up of are
suspended mass between a pair of capacitive plates. When the sensor is tilt the mass
creates a difference in electric potential which is measured as change in capacitance.
These devices are made using the techniques of micro fabrication. This technology
based on silicon And germanium using surface micromachining processes and using
bulk machining.

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To converter digital signal in to voice we use DF play mini mp3 module, it is a small
simple mp3 model which output is directly connected to the speaker. This mp3 can used
with any microcontroller having RX/TX pins and also used in stand-alone mode when
switches and batteries attached. It is serial MP3 module which supportsFAT16 and
FAT32 file system, it also supports TF card driver. It is easy to use we can simply
specify the music file through simple serial commands.
Speakers are measured in terms of decibels pressure level per watt amplifier power
measured at 1 meter from speaker. Total harmonic distortion measures the distortion
wood uses when speaker translate voltage into sound the values between 0.05% and
0.8%THD means a quality speaker. Speaker impedance measures the current speakers
will draw.8 ohm is standard where as 4 ohm is very good but usually a lot more
expensive.

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3.3 ProcessFlow:
Fig 3-1: Process Flow of smart glove
FLEX
SENSORS
ACCELERO
METER
ADC (IN –BUILT)
MICROCONTROLLER
AT-MEGA 16
MICROCONTROLLER
AT-MEGA 328P
DF PLAYER MINI
(SOUND MODULE)
MODE
SELECTION

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In the proposed design, on the very first step sensors take the readings in results of
bending and movement of hands. Which is produced by the change in resistance ,is
measured by Voltage divider which means two resistance connected in series , one in
flex sensor which is very similar to potentiometer and the other one is fixed resistance
of 33k ohms, where as one resistance is connected to 5v which is flex sensor and the
second pin of flex sensor is connected with ADC and with the fixed resistance which is
ground.
The built in ADC of AT mega 16 microcontroller has 10 bit of resolution which means
the value in decimal value varies from 0-1023 .The reference voltage is the maximum
value which we received for example if 5 volt is the reference voltage in our case ,so
the step size is 4.88 mV
Formula for Calculating Step size
Step size = vref / 2n
Where n is number of resolution bits
The step size defines the detectable change which hardware can read. In the certain case
, after every 4.88 mV increment or decrement creates a unit change in decimal value
The values from in-built ADC went to ALU of the microcontroller where processing
starts according to program where decision were held in the microcontroller. The output
of the microcontroller sends the output serially to small microcontroller which contains
the library of DF mini mp3 player which is serial mp3 module which is our main output
that converts digital signals into voice.
The communication between microcontrollers is I2C protocol. This protocol was
proposed by Philips semiconductor in 1982.It is used in microprocessors and
microcontroller usually for short distance and intra-board communication. It is
appropriate for low cost manufacturing and simple devices where slow speed can be
afforded. Some of its applications are low speed ADC and DACs, reading diagnostics
sensors and hardware monitoring etc. The main aspect of I2C is power of a micro
controller to control a collection of devices only with 2 input output general pins where
as a numerous technologies for this purpose such as SPI, but they took a lot of pins.

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To use this gadget in the different places, many gestures have different meanings in
different places along with the change in different places needs also become different.
For example: In office we have different demands and whereas these demands change
in restaurants. To overcome these types of needs we come up with a mode selection
mode. By selecting your mode, with the change in place, we can come up with same
gestures but with your desired voice. We can select modes with the help of switches
build in our board .It has four switches means we have four selection modes.
There is a separate PCB for accelerometer and flex sensors where these sensor are
attached and by the help of 8 wires. We bring the sensors parameters to the main board
where the above mentioned process starts.

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3.4 Schematics:

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3.5 Algorithm:
The algorithm which we partially implemented is a standard clustering algorithm
known as k-means clustering algorithm. This algorithm is generally used for signal
processing and data mining . It differentiates the observed the different classes of data
with the nearest mean which is set standard by the user. This algorithm make a strong
the relationship to those data which lies in between two classifier .Sometimes people
confused this algorithm with machine learning technique. Jame MacQueen was the
person who used k-means term. The standard was set by Stuart Lloydin 1957 and it was
published in 1982.
Take each set of data whose mean has the least squared distance, which is known as the
nearest mean. Then this mean is differentiated with standard prototype entered by the
user or the data which we collected as masterpiece or standardized it. By the difference,
we can analyze our random generated data lie to which class of standard prototype or
master piece data.

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Start
Select Mode
Define I/O pins
Read the analog values
of sensor 4 time
Take the average of
these 4 values
Subtract these values
with the pre-defined
Find the least
difference
among the all
pre-defined
Play the corresponding
gesture voice
Wait for the voice to
complete
Initialization of all devices
NO
YES

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In our implemented algorithm, we take all the sensors reading in arrays four time in
every loop. Once all the four arrays filled with sensors readings we take their mean
which is save into other array. The mean valued array is subtracted with the standard
values corresponding to all gestures. The mean valued array is subtracted one by one
with all the recorded value of corresponding gestures and their results were saved in to
the new arrays. Then it compares the values with all the record values whose data set
has the minimum difference it will generates the output for respective data.
The advantages of this algorithm is that it reduces the noise and garbage data and it
enhance the selection range of desired data and it very sensitive to the outliners means
the data which just crossed the detection range . The drawbacks of using that data is
that it needs some time consuming processing. It is complex and requires accuracy.
The results are not repeatable but lack in consistency where as other methods are very
consistent. Conclusively, we can say that it most efficient and less complex way
towards the desired project.

18
3.6 Simulation:
During the whole procedure, we have done several simulation processes, in many cases
we failed to find the desired result. The circuit designing is tested in Proteus which is
good simulation tool. We have find and analyze the sensors response and behavior of
analog sensors on matlab using real-time interfacing with arduino. In some parts, we
were not able to find the certain tool for simulation, so we analyze them on the
breadboard.
3.6.1 Real-time interfacing of sensors
In this below figures, we analyzed the response of accelerometer in all 3-
dimensions.The curves shows that analog values in that accelerometer in responding
with respect to time. In this simulation, we use matlab interfacing with arduino which
gives the live response of these values and plot it accordingly.
Fig 3-4: ADXL335 testing

19
Fig 3-5: ADXL335 real time plot on Matlab
Similarly, the same procedure will be done with the all five sensors and check their
real-time response of them with respect to time and with the change in movement of
hands and with different gestures.
Fig 3-6: Flex sensor testing

20
Fig 3-7: Flex sensor real time plot on Matlab
In the below picture, we analyze the our code of AT-mega 16 for the mode purpose which a
part of our design, the instructions execution results can be shown by help LEDs. This
simulation is done on proteus. In the way we check our code and hardware design is parallel
and the response time also.
Fig 3-8: Mode testing simulation

21
In the second simulation, we test the communication and between arduino and AT- mega 16
using I2C protocol, by which the main output device DF mini mp3 will played by TX, RX pin
of arduino. So , we test it by simply putting the led at the output pins
Fig 3-9: Communication testing between AVR and Arduino
In the below mentioned circuit, we play the DF Mini MP3 player with help of arduino
by calling mp3 files using the instructions programmed in arduino.
Fig 3-10: DF mp3 mini player testing

22
3.7 PCB design:
Fig 3-11: Front viewPCB schematic of main board
Fig 3-12: Rear view PCB schematic of main board
Fig 3-13: Front and rear view PCB schematic of sensor channel

23
3.8 Mechanicaldesign:
3.9 Hardware and software:
In our project the hardware contain different component which consist of
microcontroller, sensors and sound module. We selected very high quality component
to make our project very precise and accurate. Without good quality component the
output result of the smart glove can be change and varies when wear by different person.
So we do not compromise on the quality of the component. Following are the main
components which we use in our project.
3.9.1 AVR microcontroller:
In the market the available microcontroller which are of good quality and of low cost
are AVR family and PIC family. We select AVR microcontroller in our project instead
if PIC because PIC microcontroller take 4 cycle to execute one instruction. While AVR
microcontroller took cycle for the execution of one instruction. So it is one of the reason
why we selected AVR microcontroller. There is another reason for the selection of
AVR microcontroller and that is we need 8 analog pin of a microcontroller because we
in our project we the sensors we uses have 8 analog output. So we selected the
microcontroller of AVR family which ATMEGA 16.
Fig 3-14: ATMEGA16 Microcontroller

24
Fig 3-15: ATMEGA16 Microcontroller pin out diagram
3.9.2 Flex sensor:
We have used two sensors in our project. One sensor for the detection of bending of
fingers and the second sensor for the motion of hand. To detect the bending of fingers
we used flex sensor. It is just like a variable resistor whose resistance changes with
respect to the amount of the finger bending. Flex sensor is an analog sensor and its
value increases whenever flex sensor bends. We placed the flex sensor on the each
finger of leather glove to detect bending of the fingers.
Fig 3-16: Flex sensor

25
3.9.3 Accelerometer:
The second sensor which we used to detect the motion of the hand is accelerometer
which is ADXL335. We placed this sensor on the upper face of the leather glove. This
sensor help us a lot and the give values of the change in the motion by the hand which
is the x-axis, y-axis and z-axis.
Fig 3-17: ADXL335 Accelerometer
3.9.4 DF mini mp3 player:
To play the voices we need a sound module in our project. That why we selected a
sound module which is DF mini mp3 player. This sound module is directly connected
to speaker at the output. This sound module has the capability to run with any
microcontroller which have RX/TX pins and can be use as a standalone. It supports
FAT16 and FAT32 file system. It can also support a microSD card upto 32GB.
Fig 3-18: DF mp3 mini player

26
3.9.5 Arduino nano:
The arduino nano is very small, proper and breadboard friendly microcontroller with a
onboard chip Atmega328. The input range of arduino nano is between 7-12 volts. The
arduino nano has a flash memory for storing code upto 32KB with 2KB used for
bootloader. It has a clock speed of 16Mhz .The nano contain 2KB of SRAM . It also
has 1KB EEPROM. The ardiuno nano has 8 analog pin for the input and has 14 digital
pins which can be used whether as both output or as an input. In 14 digital pin 5 of them
can be used by PWM. The nano has a resolution of 10 bits (i.e. 1024 different values).
Fig 3-19: Arduino nano
Our project smart glove for deaf and dumb is based on both the hardware and the
software. We used different software for the simulation and test purpose to make our
more efficient. For simulation purpose we used software like proteus and matlab for
different simulation in our project. For a PCB purpose the software which we use is
EasyEda. The software on which we have done our project programming is Atmel
Studio.
3.9.6 Proteus:
Proteus is a complete solution software for different circuit analysis and simulation and
also for PCB design. It helps the design engineer in the workflow and help the product
to get into the market much faster. Proteus is very easy and a powerful tool to use. In
proteus, features like autorouting, 3d visualization and design parts save a lot time
during production of the project. To do our project efficiently we divide the circuitry of
our project in different parts. And then simulate those circuit parts step by step in
proteus.

27
3.9.7 Matlab:
Matlab is another software which we use to simulate real time simulation. Matlab
provides a development environment that offers to perform high numerical calculation
and computation, to perform different data analysis, visualization capabilities and
product development tool. You can calculate the calculation very immediately so they
are tested as you do. Matlab gives access to thousand of different built in fundamental
and special functions which is written by experts. In our project with the help of matlab
we simulate real time graph of five flex sensor at a time which is placed on each finger
of the hand. Also we simulate the real time graph of motion detection sensor which is
accelerometer on matlab to give the output value of x-axis, y-axis and z-axis in real
time.
3.9.8 EasyEDA:
We make our project circuit on a PCB board to make our project more efficient and
accurate. The software which we use to make the PCB for our circuit is EasyEda. It is
very user friendly and a tool which is very easy to used. We make PCB of our project
on a single layer.
3.9.9 Atmel studio:
The software which we used for the programming of our microcontroller is Atmel
studio. Atmel studio is one of the best software to program AVR microcontroller. The
atmel studio allow to import sketches from arduino ide or c/c++ program. It supports
upto more than 500 AVR and SAM devices. Huge amount of built In libraries , drivers
and more than thousand example with source code help and ease the user while doing
their projects on atmel studio .

28
3.10 Safety& precautions:
Smart glove provides a lot of help in removing the communication barrier between deaf
and dumb with the normal people so they easily understand each other. So it provides
opportunity to deaf and dumb community to express themselves more to the society.
But there are many safety and precautions that should be followed while using the smart
glove for the long time use. Following are the few safety and precaution while using
the smart glove by a disable person.
 Smart glove should not be used while eating or drinking.
 Smart glove should not be used while carrying something in the hands.
 Smart glove should be used while the hand are wet.
 Smart glove should be keep in a dry place while not in use.
 Protect smart glove from long time exposure to sun.
 Smart glove should not be used while cooking.
 Smart glove should not be used while washing clothes.
 Smart glove should not be while doing any sports activity for e.g. playing
cricket, football ,swimming etc.
 Smart glove should be used while driving.
 Smart glove should not be while doing some electrical maintenance.
If the user of the smart glove follow these safety and precaution without any hesitation
and ignorance of these point then they will take long time service from a smart glove.

CHAPTER 4 TEST RESULTS AND THEIR ANALYSIS
29
CHAPTER 4
4 TEST RESULTS AND THEIR ANALYSIS
In our design, the detection time for sensor is 2 seconds for anyone who wears this
glove. Each sign of this language based on the movement of hands and bending fingers
in specific manner with particular angle respectively. The values which are generated
by flex sensor and accelerometer are given to ADC channel of microcontroller. These
sensors produce different values on the basis of position of hands and fingers. Each
produces a unique value which is already assigned to microcontroller, by using least
distance technique, the microcontroller finds the nearest value among the all defined
gestures. Once, it is identified, the signal is send to other microcontroller to generate
the output. For every identified value the microcontroller generates the respective voice.
The values are checked on the LCD which is connected with microcontroller. The
analog values according to some of our gestures are given below
Table 4-1: Gesture values of Flex sensor
Gesture Sensor-1 Sensor-2 Sensor-3 Sensor- 4 Sensor-5
1 350 161 301 199 448
2 361 166 218 155 342
3 348 266 190 200 448
4 311 272 309 187 310
5 358 191 228 192 425
6 362 245 199 188 406
7 318 206 238 224 438
8 315 179 200 185 450
9 315 212 225 190 394

30
Gesture X-axis Y-axis Z-axis
1 365 404 275
2 267 361 397
3 365 360 330
4 295 401 300
5 400 350 420
6 406 300 247
7 367 275 400
8 425 364 299
9 300 320 290
Table 4-2: Gesture values of accelerometer
The above table shows the values of sensors for some of our defined gestures. The
gesture which lie in these values or near to these values produces the voices to
respective gesture.
These are some of our gestures which we applied in our project with their meanings:
Fig 4-1: Gesture 1

31
Fig 4-2: Gesture 2
Fig 4-3: Gesture 3
Fig 4-4: Gesture 4

32
Fig 4-5: Gesture 5
Fig 4-6: Gesture 6
Fig 4-7: Gesture 7

33
Fig 4-8: Gesture 8
Fig 4-9: Gesture 9

34
4.1 Analysis:
The duration between the motion of hand gestures and the detection of it mainly
depends on the accuracy of the user to make a certain gesture. If the subject makes the
gesture at first attempt there will be no delay, and if subject makes false gestures or
wrong bending, there will be span of silence till the right gesture. Moreover, often the
sensor start giving anomalous behavior due to humidity and some other reasons which
causes problem n detecting the gesture. The third factor that disturbs the sensors reading
is the voltage fluctuations means not properly charged battery or over voltages also
effect the system.

CHAPTER 5 ECONOMIC ANALYSIS
35
CHAPTER 5
5 ECONOMIC ANALYSIS
5.1 Prototype cost
Sr. # ITEM: Specification / Description Rate Amount
1 Multimeter 3000 3,000.00
2 Soldering Iron 500 500.00
3 Battery 1500 1,500.00
4 Solder paste 100 100.00
5 Solder wire 300 300.00
6 USB AVR ISP 1000 1,000.00
7 Arduino Uno 800 1,600.00
8 Glove 250 500.00
9 Flex Sensor 3000 12,000.00
10 Resistors 200 200.00
11 AVR Microcontroller 32 Mega 500 1,000.00
12 Ic Base 75 150.00
13 Accelerometer 1000 1,000.00
14 Vero Board 50 150.00
15 Bread Board 125 250.00
16 Tape 75 150.00
17 Jumper Wires 600 600.00
18 Connectors 15 150.00
19 Arduino Mega 1500 1,500.00
20 AVR Microcontroller 16 Mega 500 1,000.00
21 Flex Sensor 3000 9,000.00
22 Power Supply 3500 3,500.00
23 AVR Burner 3000 3,000.00
24 Wire Bundle 1000 1,000.00
25 Accelerometer 1000 2,000.00
26 Arduino Uno 800 1,600.00

36
27 7805 Ic 20 40.00
28 Sound Module 3000 3,000.00
29 Memory Card 800 800.00
30 Lithium Battery 1600 1,600.00
31 Battery Charger 500 500.00
32 Data Bus 300 300.00
Table 5-1: Component Cost
5.2 Marketanalysis
Deaf/Mute population in Pakistan
Provinces Total Urban Rural
Khyber Phaktoon
Khuwa
28895 24638 4253
Sindh 57473 23236 34181
Punjab 7672 6134 1542
Balochistan 149235 109214 40033
Islamabad 1020 486 534
*Census Report Pakistan 1998,Pakistan Census Organization, GoP
State of World Refugees and UNHCR Global Report 2002, UNHCR
Table 5-2: Dumb and Deaf population statistics
These statistics shows the number of effected people in Pakistan last census held in
1998. Since Pakistan population increased very rapidly in last decade so that also
increased in number of these handicapped people. This is considered to be huge market
to hit.
To incorporate this market, we have to follow some design methodologies which must
have minimized the cost, area and power consumption. Hardware design methodology
is the most efficient methodology for our proposed design.

37
The steps of this methodology is described via flow chart
This model gives you an ASIC (Application Specific Integrated Circuit).Since, it is
designed for particular applications it has all the embedded system constraints .The
fabrication of ASIC is too much expensive depending on which technology node ,it is
fabricated.
On 180nm node, it takes atleast 20k dollars . If we produced it in 10k quantity the unit
price of an ASIC will be 2 dollars only.
Table 5-3: Marketing Analysis
Expenditures Cost per piece in Rupees Cost per 1000 pieces in
Rupees
ASIC 250 250,000
Sensors 6000 60,00,000
Other expenses and labor 600 600,000
Maintenance 100 100,000
TOTAL 6,950 92,00,000
Layout
HDL code or Any
hardware design
State assignments,
minimization etc
Architecture independent
optimization
Architecture dependent
optimization
Place and Route
Cell
librar
y
Technolog
y Database
Routability Model
Timing Analysis
Wiring Model
Timing Analysis
Timing Analysis

38
Profit Percentage:
Profit %= (production price/sell price*100)-100
=((6,950/8,000)*100)-100 =13.125%
Profit on 1000 piece will be Rs. 12,07,500/=

CHAPTER 6 CONCLUSION
39
CHAPTER 6
6 CONCLUSION
Sign language is the only way and method for dumb and deaf people to communicate
with others and convey their messages. Deaf and dumb people uses hand gestures
instead of sound to express their feeling and convey their messages. They
simultaneously combines movement in hand, arm and also facial expressions. But they
find difficulty because majority of normal people do not know about sign language and
how to understand it. Also there are not much interpreter and translator available which
can assist the large of deaf and dumb people to convey their messages to others. so we
proposed a project which is called Smart glove which is very helpful and useful for deaf
and dumb people in order to communicate with the normal people and can express
their feeling. Our project Smart glove will remove the communication barrier between
mute and deaf people with normal people. Smart glove is very cost effective system
which can give voice to deaf and mute. Smart glove is an independent glove designed
for the help of deaf and dumb person and to serve humanity. The smart glove has the
capability and capacity to translate the sign language gestures into its sound. Smart
glove uses the principle of component analysis in order to classify and gives the output
result and data in the real time. In this project we uses a leather glove which is fitted
with flex sensor on each finger along the length of each finger. The flex sensor output
value changes according to the amount of the bending of the fingers movement. Also
we placed an accelerometer on the backside of the hand to detect the motion of the hand
in all three axis. In this project we uses a microcontroller which takes the reading from
all these sensor and then recognize the all the hand gestures which is made by the user
and then give signal to another microcontroller to play the sound of that certain gesture
which is just made by the user. This project is also a social initiative step which is taken
to help deaf and dumb people and remove the communication gap between disable
person and the normal person. By using this smart glove there will be no need of any
interpreter further more. Thus this project is up lifting and encourages the community
of deaf and dumb people and ensures that they will get every opportunity they deserve
in our society without any hesitation and problem. Thus our project also eliminates the
social stigmas and standards which prevail and generates in our society.

CHAPTER 7 FUTURE RECOMMENDATION
40
CHAPTER 7
7 FUTURE RECOMMENDATION
Our project smart glove hand gesture vocalizer plays a very Important role in removing
the communication obstacle between deaf and dumb with a normal people. It helps the
deaf and dumb people to convey their messages, share their feeling and provide an
opportunity to express themselves to others. Our project has a lot of future
recommendation for example we can extend the usability of hand gestures by adding
more numbers of gestures which will translate the gestures into different languages
mode according to the different location.
Today the world is full of technologies and it is better every second of our life. Virtual
reality is one of those invention .Hand gesture can play a very important in the virtual
reality application for example we can replace the conventional input method in video
games like joystick into physical hand gestures control. So that the user can feel more
reality and can enjoy the game by a whole new way.
We can make a hand gesture robot control system which can control different machine
activities by hand gestures at sensitive places like a tele-operator robot which an assist
the doctor while they are doing the surgeries.
Internet of thing is also another new technology of modern world. By using internet of
thing a doctor can monitor its patient health on a mobile via app or on a computer. A
doctor can monitor anywhere the heart beat, blood pressure etc regardless the presence
of patient.
We can make the smart glove wireless so that the user can feel more ease and
comfortability while using our widget. We can make a system by using hand gestures
to control different home appliances like fan, light , TV etc from anywhere at home just
by making a hand gesture.
Hand gesture can a very helpful for a paralyze person which are on a wheel chair. By
just making a gesture a paralyze person can control the wheel chair just by own hand
and do not have to drive the wheel chair.

CHAPTER 7 FUTURE RECOMMENDATION
41
There are a lot of people who do not understand sign language. So by using smart glove
we can teach a normal person to learn sign language so it will be helpful both the normal
person and disable person to easily communicate and understand each other. We can
also make a digital board which can be mostly use by a teacher. Teacher do not have to
write anything on the white board by own hand. They can teach the student just by
sitting on their chair and can write anything on digital board by hand gesture.

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