delna's journal

International Journal of Advanced Research Trends in Engineering and Technology
Vol. II, Special Issue X, March 2015
HOLY GRACE ACADEMY OF ENGINEERING
NATIONAL LEVEL CONFERENCE ON INNOVATIVE ENGINEERING
All Rights Reserved © 2015 IJARTET
DIGITAL VOCALIZER
Delna Domini
B.Tech, Electronics and Communication, Holy Grace Academy of Engineering, Mala
Asst. Professor, Electronics and Communication, Holy Grace Academy of Engineering, Mala
Asst. Professor, Electronics and Communication, Holy Grace Academy of Engineering, Mala
Abstract: This paper presents a gesture recognition model; known as Digital Vocalizer. Digital Vocalizer is an arduino UNO
based system and this system is designed to make the communication gap between dumb, deaf and blind communities and their
communication with the normal people as less as possible. Here in this paper, we mentioned the people by normal people who
have the abilities to see, hear and talk. Deaf people make use of the sign languages or gestures to convey what he/she needs to
say; but it is impossible to understand by a person who can hear. So we come on a conclusion to make an easy prototype by
taking some of those gestures and convert them into visual and audio forms. So the communication gap can be reduced as much
as possible. For that we make use of arduino UNO Board as Atmega 328 Controller to interface all of the actuators and sensors.
Sensors are fixed on the palm and fingers. Those sensed values will give the information about the parameters like finger ben
hand position angle; and these are converted into electrical signal and switch to arduino UNO. Arduino UNO takes the actions
according to the gestures.
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al of Advanced Research Trends in Engineering and Technology
Vol. II, Special Issue X, March 2015 in association with
ORGANIZES
(16-20
TH
MARCH 2015)
DIGITAL VOCALIZER
Delna Domini1
, Sreejith s2
, Aksa David3
B.Tech, Electronics and Communication, Holy Grace Academy of Engineering, Mala,
Professor, Electronics and Communication, Holy Grace Academy of Engineering, Mala
Professor, Electronics and Communication, Holy Grace Academy of Engineering, Mala
This paper presents a gesture recognition model; known as Digital Vocalizer. Digital Vocalizer is an arduino UNO
e normal people as less as possible. Here in this paper, we mentioned the people by normal people who
Deaf people make use of the sign languages or gestures to convey what he/she needs to
to understand by a person who can hear. So we come on a conclusion to make an easy prototype by
arduino UNO Board as Atmega 328 Controller to interface all of the actuators and sensors.
Sensors are fixed on the palm and fingers. Those sensed values will give the information about the parameters like finger ben
nverted into electrical signal and switch to arduino UNO. Arduino UNO takes the actions
ISSN 2394-3777 (Print)
ISSN 2394-3785 (Online)
Available online at www.ijartet.com
al of Advanced Research Trends in Engineering and Technology (IJARTET)
676
, India
Professor, Electronics and Communication, Holy Grace Academy of Engineering, Mala, India
Professor, Electronics and Communication, Holy Grace Academy of Engineering, Mala, India
This paper presents a gesture recognition model; known as Digital Vocalizer. Digital Vocalizer is an arduino UNO
e normal people as less as possible. Here in this paper, we mentioned the people by normal people who
Deaf people make use of the sign languages or gestures to convey what he/she needs to
to understand by a person who can hear. So we come on a conclusion to make an easy prototype by
arduino UNO Board as Atmega 328 Controller to interface all of the actuators and sensors.
Sensors are fixed on the palm and fingers. Those sensed values will give the information about the parameters like finger bend,
nverted into electrical signal and switch to arduino UNO. Arduino UNO takes the actions

Keywords: Include at least 4 keywords or phrases
I. INTRODUCTION
Communication is a crucial part of human beings
lack of proper communication will cause severe problems.
The communication is mainly done by gestures and speech.
So a complete coordination of these two is necessary.
number of hardware techniques are used for gathering
information about body positioning; typically either image
based (using cameras, moving lights etc) or device
(using instrumented gloves, position trackers etc.), although
hybrids are beginning to come about [1], [2], [3].
However, first we need to get the data; after that
we need to recognize those detected data from the glove and
it is considered as the second step. And the researches on
this are in progress. This research paper deals with the data
from a digital data glove for the use of recognition of
gestures. This system converts these signs and into
and speech. The conversion of data to visual and speech is
achieved by the audio processor.
II. BACKGROUND
A lot of research works have been done in the field of
gesture recognition; many are there in prog
recent research; “Recognition of Hand Gestures Using
Range Images” is explained in reference [1]. Reference [2]
gives a small frame work of the Hybrid Classifiers
Reference [3] gives a brief explanation of classification
Gesture. “Microcontroller and Sensors Based Gesture
Vocalizer” depicts a vocalizer by using 8051
microcontroller. In reference [4] a Survey
Recognition has been done in reference [6].
description about the Data Glove is there on reference
The methods to improve the recognition are being framed in
reference [8]. The requirements of a
recognition system are explained in reference [9].
Hand Tracking technology is explained in reference
Real-Time Gesture Recognition system is discussed in
reference [11]. Reference [12] gives an analysis of Dynamic
Hand Gesture Recognition System. Reference [13] shows a
system with multiple sensors which will give more accurate
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Include at least 4 keywords or phrases
part of human beings life; the
lack of proper communication will cause severe problems.
by gestures and speech.
So a complete coordination of these two is necessary. A
number of hardware techniques are used for gathering
information about body positioning; typically either image-
based (using cameras, moving lights etc) or device-based
instrumented gloves, position trackers etc.), although
hybrids are beginning to come about [1], [2], [3].
However, first we need to get the data; after that
we need to recognize those detected data from the glove and
it is considered as the second step. And the researches on
this are in progress. This research paper deals with the data
e for the use of recognition of
signs and into visual
conversion of data to visual and speech is
BACKGROUND
A lot of research works have been done in the field of
recognition; many are there in progress. The most
“Recognition of Hand Gestures Using
” is explained in reference [1]. Reference [2]
Hybrid Classifiers.
f classification
Microcontroller and Sensors Based Gesture
by using 8051
Survey on Gesture
has been done in reference [6]. Again a brief
is there on reference [7].
The methods to improve the recognition are being framed in
reference [8]. The requirements of a Self organized
system are explained in reference [9]. A Free
technology is explained in reference [10]. A
Time Gesture Recognition system is discussed in
gives an analysis of Dynamic
Hand Gesture Recognition System. Reference [13] shows a
system with multiple sensors which will give more accurate
values. Reference [14] explains a Nonspecific
Gesture Recognition technology. An Algorithm and its
Application for 3D Interaction is discussed in reference [15].
III. METHODOLOGY
Block diagram of the whole system is shown Fig.1. The
system consists of:
 Data Glove
 Tilt Detection
 Bend Detection
 Adriano Board
 Audio Processor
 LCD Display
Fig.1: Block Diagram of the System
Digital glove is consisted of two sensors. They are
flex sensors and accelerometer as tilt sensor. The tilt
detection module and the bend detection
output of the accelerometer and flex sensor respectively
combinations of these results will lead to the generation of
some binary addresses; and those 8-bit addresses are directly
switched to the audio processor. For
address will be different. Audio processor processes it and
speaks. The data will be sent to the LCD which displays the
word corresponding word.
IV. SYSTEM DESCRIPTION
A. Data Glove
Flex sensors and accelerometer as tilt sensors
which are placed on the data glove
accelerometer is detected by the tilt detection module, while
the output of the flex sensors is detected by the bend
detection module. The combination of these results of two
sensors is given to the arduino UNO for further operations.
677
Nonspecific-User Hand
An Algorithm and its
is discussed in reference [15].
METHODOLOGY
ystem is shown Fig.1. The
Digital glove is consisted of two sensors. They are
flex sensors and accelerometer as tilt sensor. The tilt
the bend detection module detect the
output of the accelerometer and flex sensor respectively. The
lead to the generation of
bit addresses are directly
o processor. For each gesture, the binary
. Audio processor processes it and
The data will be sent to the LCD which displays the
SYSTEM DESCRIPTION
lex sensors and accelerometer as tilt sensors are the sensors
which are placed on the data glove. The output of the
accelerometer is detected by the tilt detection module, while
the output of the flex sensors is detected by the bend
detection module. The combination of these results of two
given to the arduino UNO for further operations.

This combined result will give the overall gesture of the
hand.
I: Bend sensor
‘Flex Sensor’ or ‘Bend Sensor’ is a sensor that changes its
resistance depending on the amount of bend on the sensor. In
this system, five flex sensors are there on the data glove
This are stitched on each of the finger of the hand glove
they records the static and dynamic movements of the
fingers. Fig.2 shows how a flex sensor looks like.
usually in the form of a thin strip from 1”
in resistance range.
Fig.2: Flex Sensor
The Flex sensors are mainly made up of
Carbon elements. Flex sensors have great form
sensor produces a resistance output according
radius. As the radius reduces, the resistance value increases.
They convert the change in bend into electrical resistance.
As the bend increases the resistance value also increases.
The Change in resistance with increasing
Fig.3.
Fig.3: Change in resistance with increasing bend
In simple words, flex sensors are analog resistors.
They work as variable analog voltage dividers and it is
depicted in fig.4.
Fig.4: flex sensors as variable analog voltage dividers
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This combined result will give the overall gesture of the
Flex Sensor’ or ‘Bend Sensor’ is a sensor that changes its
resistance depending on the amount of bend on the sensor. In
are there on the data glove.
This are stitched on each of the finger of the hand glove and
movements of the
fingers. Fig.2 shows how a flex sensor looks like. They are
usually in the form of a thin strip from 1”-5” long that vary
The Flex sensors are mainly made up of resistive
great form-factor. The
output according to the bend
radius. As the radius reduces, the resistance value increases.
They convert the change in bend into electrical resistance.
As the bend increases the resistance value also increases.
Change in resistance with increasing bend is shown in
In simple words, flex sensors are analog resistors.
They work as variable analog voltage dividers and it is
dividers
As the amount of carbon increases the
decreases. Circuit diagram of flex sensor is shown below in
Fig.5.
Fig.5: Circuit diagram of flex sensor
The main applications of flex sensors are
Robotics, Automotive controls, Fitness Produ
Devices, Animatronics Virtual Reality gaming consoles
II: TILT SENSOR
In this system, Accelerometer is used to sense
slanting of the hand. An accelerometer is a device that can
measure dynamic acceleration (vibrations) and static
acceleration (gravity). Being that it measures this, it can
sense movements/lack-of-movements, tilts and even
rotation also. A microcontroller is required to read
record the tilt. Here in this system, we are using arduino
microcontroller. The unit of the output of the accelerometer
is mg (Milligram). Hence the mapping of the readings is
necessary. Thereby we can display
computer.
ADXL335 accelerometer is used in this digital
vocalizer. The Output of accelerometer is analog and it
ranges from 1.5 to 3.5 volts. The output of
is given to the arduino. ADXL335 interface with Arduino
678
increases the resistance
Circuit diagram of flex sensor is shown below in
The main applications of flex sensors are
Fitness Products, Medical
irtual Reality gaming consoles etc.
is used to sense the tilting or
An accelerometer is a device that can
measure dynamic acceleration (vibrations) and static
acceleration (gravity). Being that it measures this, it can
movements, tilts and even
rotation also. A microcontroller is required to read and
Here in this system, we are using arduino
The unit of the output of the accelerometer
Hence the mapping of the readings is
display these readings on the
accelerometer is used in this digital
Output of accelerometer is analog and it
from 1.5 to 3.5 volts. The output of the accelerometer
ADXL335 interface with Arduino is

shown below in Fig.6.
Fig.6: ADXL335 Interface with Arduino UNO
Six pins are there on the ADXL335 accelerometer. It is
shown in table.1. Two pins are there for
ground, X, Y and Z pins are the analog pins. The last pin
known as test pin can be left as disconnected.
supply must be up then wire up the analog pins
inputs of microcontroller.
TABLE I
PIN DESCRIPTION OF ADXL335
B. Gesture Detection
Gesture recognition is handled by arduino UNO.
function of arduino UNO is to compare the detected values
by the sensors with the predefined values.
comparison it sends some binary addresses
the Audio processor. The arduino UNO would have two
input sections; they are from the flex sensors and
accelerometer.
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shown below in Fig.6.
Interface with Arduino UNO
on the ADXL335 accelerometer. It is
power supply and
X, Y and Z pins are the analog pins. The last pin
known as test pin can be left as disconnected. First power
the analog pins to the analog
ADXL335
Gesture recognition is handled by arduino UNO. The main
to compare the detected values
According to that
binary addresses to the LCD and
the Audio processor. The arduino UNO would have two
input sections; they are from the flex sensors and from the
Fig.6: arduino UNO
Each flex sensors’ output is given to the separate analog pins
of arduino board. At first the output of these five flex
sensors are being evaluated and pulse width is calculated,
after the evaluation of all the five flex sensors they are
combined with the values of significant tilt /slant. Flex
sensor is very sensitive. Even a small bend
will give infinite levels of bends. Hence the values of
bending are quantized into ten levels. This makes
determine how much bending of the finger has happened.
Since the outputs of both the sensors are analog,
this is not measurable by the microcontroller. It is necessary
to convert it into digital form. A lot of
converter ICs (ADC) are available in the market. However,
in this system, we are using arduino UNO which has inbuilt
ADC. These two digital values are being evaluated in the
further more steps.
As a continuation of the above steps, the arduino
checks the digital output from the ADC. The
the data which is received from the ADC’s
Arduino. Significant means that hand is delivering some
meaningful gesture. The microcontroller compares the
output of the ADC with the already stored values.
to this comparison, the arduino determines that,
gesture is significant or not.If it is a significant gesture, the
microcontroller moves on to the next step;
binary data to the audio processor and LCD
C. Audio Processor
Audio processor speaks out against the significant gesture.
Here we are using aPR33A3. It is capable of producing 8
voice messages. For that the voice messages must be
recorded. To record these messages, a microphone can be
used. A lot of microphones are available in the market. Here
in this system an electret microphone is used.
I. ELECTRET MICROPHONE
679
Each flex sensors’ output is given to the separate analog pins
of arduino board. At first the output of these five flex
sensors are being evaluated and pulse width is calculated,
the five flex sensors they are
combined with the values of significant tilt /slant. Flex
sensor is very sensitive. Even a small bend is monitored it
will give infinite levels of bends. Hence the values of
bending are quantized into ten levels. This makes easier to
determine how much bending of the finger has happened.
Since the outputs of both the sensors are analog,
this is not measurable by the microcontroller. It is necessary
to convert it into digital form. A lot of Analog to digital
in the market. However,
in this system, we are using arduino UNO which has inbuilt
ADC. These two digital values are being evaluated in the
As a continuation of the above steps, the arduino
m the ADC. The significance of
received from the ADC’s is checked by the
. Significant means that hand is delivering some
meaningful gesture. The microcontroller compares the
output of the ADC with the already stored values. According
the arduino determines that, whether the
significant or not.If it is a significant gesture, the
microcontroller moves on to the next step; the eight bit
binary data to the audio processor and LCD will be sent.
against the significant gesture.
s capable of producing 8
voice messages. For that the voice messages must be
recorded. To record these messages, a microphone can be
microphones are available in the market. Here
in this system an electret microphone is used.

A microphone is a transducer which converts
to electrical signals. Transducers are devices
energy from one form to other. Its working
opposite to a speaker. They are available in
and sizes. Commonly used microphone is
microphone. It is shown in Fig.9. It is
phones, laptops, etc. It is mainly used to
sounds or air vibrations. The top of the electret microphone
is covered by a porous material. It filters out the
particles.
Fig.9: Electret Microphone
The two legs as shown in fig.9 are used to make
electrical connection with the circuit. The sound signals/air
vibrations pass through the porous material and falls on the
diaphragm through the hole at the centre.
capacitance due to the presence of sound waves
variance in voltage and sends corresponding el
signals. The frequency response of electret microphone
shown in Fig.11. Test circuit for microphone and amplifier
modules is shown in Fig.12
Fig.11: Frequency response curve of electrets microphone
Fig.12: Test circuit for microphone and amplifier modules
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converts sound energy
devices which convert
working principle is
in different shapes
electret condenser
is used in mobile
mainly used to detect minor
top of the electret microphone
filters out the dust
The two legs as shown in fig.9 are used to make
The sound signals/air
vibrations pass through the porous material and falls on the
diaphragm through the hole at the centre. This change in
due to the presence of sound waves produces
orresponding electrical
of electret microphone is
Test circuit for microphone and amplifier
requency response curve of electrets microphone
Test circuit for microphone and amplifier modules
If your supply is 5v, you need to bias the output to
2.5v to get maximum swing from your input signal. So a
pre-amplification can be used along with microphone signal
in order to activate the analog input
shown in Fig.13. The output waveform of the microphone
with the pre-amplification circuit is shown in Fig.14.
.
Fig.13:Pre-amplification circuit for Electret Microphone
Fig.14: The output waveform of microphone with
Arduino generates the 8 bit addresses
the data from the bend and tilt detection modules. Now the
arduino can understand the meaning of each gestures and it
is capable to send the corresponding message address
need to generate the voices to each significant gesture,
switch these 8-bit addresses to the audio processor IC
we are using aPR33A3 audio processor
680
If your supply is 5v, you need to bias the output to
2.5v to get maximum swing from your input signal. So a
amplification can be used along with microphone signal
in order to activate the analog input of arduino and it is
shown in Fig.13. The output waveform of the microphone
tion circuit is shown in Fig.14.
amplification circuit for Electret Microphone
Fig.14: The output waveform of microphone with pre-amplification circuit
ino generates the 8 bit addresses according to
the data from the bend and tilt detection modules. Now the
arduino can understand the meaning of each gestures and it
e corresponding message address. As we
to each significant gesture, we
audio processor IC. Here
we are using aPR33A3 audio processor.

Fig.15:aPR33A3 Interface with Arduino UNO
Some particular allophones are allocated to each
word and for those allophones some addresses are defined in
audio processor IC. These 8 bit addresses are
aPR33A3 to speak those particular words
that it is necessary to know the 8 bit digital address
word or sentence. The microcontroller sends the
to aPR33A3. The address will locate the
word. The aPR33A3 gives an output signal and it can be
amplified to make it louder. The speaker takes the o
this amplifier as the input and it speaks the voice message
corresponding to the 8-bit addresses.
D. LCD Display
The audio processor gives a provision for the
communication of dumb with the normal people and with
the blind people as well. But the communication gap
between the dump people and the deaf
reduced with the help of audio processor IC
condition a display can be attached along with the audio
processor.
Fig.16: LCD Interface with Arduino UNO
JHD162A is the LCD module used here. JHD162A is a 16×2
LCD module based on the HD44780 driver from Hitachi.
The JHD162A has 16 pins and can be operated in 4
or 8-bit mode. The microcontroller has already
generated the 8 bit address bit and it is b
audio processor, the same address is being
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Some particular allophones are allocated to each
for those allophones some addresses are defined in
es are sent to the
s. The summary is
8 bit digital addresses of each
microcontroller sends these addresses
the allophone of the
output signal and it can be
amplified to make it louder. The speaker takes the output of
the voice messages
a provision for the
communication of dumb with the normal people and with
he communication gap
people cannot be
reduced with the help of audio processor IC. To avoid this
can be attached along with the audio
JHD162A is the LCD module used here. JHD162A is a 16×2
LCD module based on the HD44780 driver from Hitachi.
The JHD162A has 16 pins and can be operated in 4-bit mode
The microcontroller has already
generated the 8 bit address bit and it is being sent to the
address is being sent to the LCD
module. An eight bit address against each
is sent to LCD module. This eight bit address decides what
should be displayed on the LCD.
V. Overview of the System
The Fig.17 depicts the over view of the Digital Vocalizer.
Fig.17: An overview of the whole system
6. Conclusion and Future Enhancements
a detailed structure of a system is designed to make the
communication gap between dumb, blind
communities and their communication with the normal
people who can speak, see and talk
sign language is being by the dump communities used for
their communication. But these sign languages cannot be
used for the communication of dump communities with the
blind as well as normal people. Here we can use this Digital
Vocalizer which will generate voice and display according
to these signs. This display will help deaf communities to
reduce the communication gap also.
The future enhancements:
1. A provision for ZigBee standard for
Vocalizer”.
2. A new design with TTS256t
storage capacity.
3. Accurate monitoring of the static dynamic
movements involved in “Digital Vocalizer”.
4. A system with whole jacket, which could
capable of conveying the movements of animals.
5. The replacement of virtual reality application like
joy sticks with the “Digital Vocalizer” (Data
Glove).
VI. REFERENCES
[1] Kazunori Umeda Isao Furusawa and Shinya
Tanaka, “Recognition of Hand Gestures Using
Range Images”, Proceedings of the 1998 IEEW/RSJ
International Conference on intelligent Robots and
Systems Victoria, B.C., Canada October 1998, pp.
1727-1732
681
against each significant gesture
is sent to LCD module. This eight bit address decides what
the System
17 depicts the over view of the Digital Vocalizer.
Enhancements This paper gives
system is designed to make the
communication gap between dumb, blind and deaf
communities and their communication with the normal
as less as possible. The
sign language is being by the dump communities used for
their communication. But these sign languages cannot be
of dump communities with the
blind as well as normal people. Here we can use this Digital
Vocalizer which will generate voice and display according
This display will help deaf communities to
A provision for ZigBee standard for “Digital
A new design with TTS256t to improve message
Accurate monitoring of the static dynamic
movements involved in “Digital Vocalizer”.
jacket, which could be
capable of conveying the movements of animals.
The replacement of virtual reality application like
joy sticks with the “Digital Vocalizer” (Data
Kazunori Umeda Isao Furusawa and Shinya
d Gestures Using
Proceedings of the 1998 IEEW/RSJ
International Conference on intelligent Robots and
., Canada October 1998, pp.

[2] Srinivas Gutta, Jeffrey Huang, Ibrahim F. Imam,
and Harry Wechsler, “Face and Hand Gesture
Recognition Using Hybrid Classifiers”, ISBN: 0
8186-7713-9/96, pp.164-169
[3] Jean-Christophe Lementec and Peter Bajcsy,
“Recognition of Am Gestures Using Multiple
Orientation Sensors: Gesture Classification”,
IEEE Intelligent Transportation Systems Conference
Washington, D.C., USA, October 34, 2004, pp.965
970
[4] Microcontroller and Sensors Based Gesture Vocalizer
Proceedings of the 7th WSEAS International Conference on
SIGNAL PROCESSING, ROBOTICS and AUTOMATION
(ISPRA '08), University of Cambridge, UK, February 20
2008
[5] Sushmita Mitra and Tinku Acharya,”Gesture
Recognition: A Survey”, IEEE TRANSACTIONS ON
SYSTEMS, MAN, AND CYBERNETICS
APPLICATIONS AND REVIEWS, VOL. 37, NO. 3,
MAY 2007, pp. 311-324
[6] Recognition of a Hand-Gesture Based on
Self-organization Using a Data Glove, 0-7803
~/99/$10.080 1999 IEEE
[7] Sanshzar Kettebekov, Mohammed Yeasin and
Rajeev Sharma, “Improving Continuous Gesture
Recognition with Spoken Prosody”, Proceedings of the
2003 IEEE Computer Society Conference o
Vision and Pattern Recognition (CVPR’03),
1063-6919/03, pp.1-6
[8] Masumi Ishikawa and Hiroko Matsumura,
“Recognition of a Hand-Gesture Based on Selforganization
Using a Data Glove”, ISBN # 0-7803-
5871-6/99, pp. 739-745
[9] Hand Gesture Recognition Using Accelerometer
Sensor for Traffic Light Control System.
Shirke Swapnali, Student of ENTC Dept., SKNCOE,PUNE
Pune,India
[10] Gloved and Free Hand Tracking based Hand Gesture
Recognition,
978-1-4673-5250-5/13/$31.00 ©2013 IEEE
[11] Toshiyuki Kirishima, Kosuke Sato and Kunihiro
Chihara, “Real-Time Gesture Recognition by Learning
and Selective Control of Visual Interest Points”,
TRANSACTIONS ON PATTERN ANALYSIS AND
MACHINE INTELLIGENCE, VOL. 27, NO. 3,
MARCH 2005, pp. 351-364
[12] Attila Licsár and Tamás Szirány, “Dynamic
Training of Hand Gesture Recognition System”,
Proceedings of the 17th International Conference on
Pattern Recognition (ICPR’04), ISBN # 1051
4651/04,
[13] A Method of Hand Gesture Recognition based on
Multiple Sensors, Fan Wei, Chen Xiang, Wang Wen
Xu ,Yang Ji-hai,978-1-4244-4713-8/10/$25.00 ©2010 IEEE
[14] Nonspecific-User Hand Gesture Recognition By
Using MEMS Accelerometer, Jayaraman D
Student Member of IEEE
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Srinivas Gutta, Jeffrey Huang, Ibrahim F. Imam,
Hand Gesture
Recognition Using Hybrid Classifiers”, ISBN: 0-
Christophe Lementec and Peter Bajcsy,
“Recognition of Am Gestures Using Multiple
Orientation Sensors: Gesture Classification”, 2004
Systems Conference
USA, October 34, 2004, pp.965-
Microcontroller and Sensors Based Gesture Vocalizer
Proceedings of the 7th WSEAS International Conference on
SIGNAL PROCESSING, ROBOTICS and AUTOMATION
ridge, UK, February 20-22,
Sushmita Mitra and Tinku Acharya,”Gesture
IEEE TRANSACTIONS ON
SYSTEMS, MAN, AND CYBERNETICS—PART C:
VOL. 37, NO. 3,
7803-5871-
Sanshzar Kettebekov, Mohammed Yeasin and
Rajeev Sharma, “Improving Continuous Gesture
”, Proceedings of the
2003 IEEE Computer Society Conference on Computer
Vision and Pattern Recognition (CVPR’03), ISBN #
Masumi Ishikawa and Hiroko Matsumura,
Gesture Based on Selforganization
ion Using Accelerometer
Shirke Swapnali, Student of ENTC Dept., SKNCOE,PUNE
Gloved and Free Hand Tracking based Hand Gesture
5/13/$31.00 ©2013 IEEE, ICETACS 2013
Kirishima, Kosuke Sato and Kunihiro
Time Gesture Recognition by Learning
and Selective Control of Visual Interest Points”, IEEE
TRANSACTIONS ON PATTERN ANALYSIS AND
VOL. 27, NO. 3,
and Tamás Szirány, “Dynamic
Training of Hand Gesture Recognition System”,
Proceedings of the 17th International Conference on
ISBN # 1051-
A Method of Hand Gesture Recognition based on
Xiang, Wang Wen-hui, Zhang
8/10/$25.00 ©2010 IEEE
User Hand Gesture Recognition By
Jayaraman D
Department of ECE,ICICES2014 -
Chennai, Tamil Nadu, India
[15] lianfeng Liu, Zhigeng Pan, Xiangcheng Li "An Accelerometer
Based
Gesture Recognition Algorithm and its Application for 3D
Interaction";
ComSIS Vol. 7, No. I, Special Issue, February 2010.
682
S.A.Engineering College,
lianfeng Liu, Zhigeng Pan, Xiangcheng Li "An Accelerometer-
Gesture Recognition Algorithm and its Application for 3D
ComSIS Vol. 7, No. I, Special Issue, February 2010.

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