Hand Gloves for Deaf and Mute Person using Flex Sensor-... Survey Ranjit A. Wagh

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International Conference on Global Trends in Engineering, Technology and Management (ICGTETM-2016)
Hand Gloves for Deaf and Mute Person using Flex Sensor- a
Survey
Ranjit A. Wagh1, Dr. U.B.S. Chandrawat2
1
Research Scholar , ECE Dept.
SVCE, Indore (M.P.)
2
Professor, ECE Dept.
SVCE, Indore (M.P.)
Abstract— Generally deaf & mute people use sign
language for communication with normal people. But
these deaf & mute person find difficulty in
communicating with normal people because normal
people does not understand sign language. This paper
gives the idea about gloves which is used on the finger
of dump & mute people .This gloves is consist of flex
sensors on each finger & thump. Whenever dump
people perform hand gesture by using sign language.
This gesture will be converted into speech (audio) &
visual (video) so that normal people can understand
what dump people said & they easily communicate
with normal people. This system is very much compact
in design, as it uses a highly integrated 16-bit PIC
microcontroller, flex sensor zigbee transmitter &
reciver.
II. LITERATURE SURVEY
A. Gesture Recognition:
Sign language is a visual language. Usually,
sign language consists of three major components:
finger-spelling; word-level sign vocabulary; and
nonmanual features. The finger-spelling is used to
spell words letter by letter. The word-level sign
vocabulary is used for majority of communication,
while the nonmanual features consist of facial
expression, position of tongue, mouth, and body. Flex
sensor based gloves systems for automatic
understanding of gestural languages used by the deaf
community. Fig1show the different American Sign
Language
Keywords — sign language, flex sensor, PIC
microcontroller.
I. INTRODUCTION
Hearing loss or deafness as it is commonly
known is defined as the partial or total inability to hear.
This issue may affect the development of language
and can be the cause of lack of communication skills.
Communication involves the exchange of information,
and this can only occur effectively if all participants
use a common language. Sign language is the
language used by deaf and mute people and it is a
communication skill that uses gestures instead of
sound to convey meaning simultaneously combining
hand shapes.
People with audition problems mostly use the
sign language as a way to communicate with others.
Sign language is a language that use hand gestures and
body language to convey meaning. Even when sign
language is widely used around the world, most of the
people without hearing issues doesn’t know how to
interpret it. It is an idea to develop a solution to aid the
deaf mute people to communicate with a person that is
not familiar with the sign language. By using glove
flex sensor that measures how bended each finger is
using zigbee transmitter & zigbee receiver. Sensor
gloves technology has been used in a variety of
application areas, which demands accurate tracking
and interpretation of sign language.
ISSN: 2231-5381
Fig1. American Sign Language
B. flex sensor:
The Flex Sensor patented technology is based
on resistive carbon thick elements. As a variable
printed resistor, the Flex Sensor achieves great formfactor on a thin flexible substrate. When the substrate
is bent, the sensor produces a resistance output
correlated to the bend radius—the smaller the radius,
the higher the resistance value. Flex sensors are
normally attached to the glove using needle and thread.
They require a 5-volt input and output between 0 and
5 V, the resistivity varying with the sensor’s degree of
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International Conference on Global Trends in Engineering, Technology and Management (ICGTETM-2016)
bend and the voltage output changing accordingly.
The sensors connect to the device via three pin
connectors (ground, live, and output). The device can
activate the sensors from sleep mode, enabling them to
power down when not in use and greatly decreasing
power consumption. The flex sensor pictured below
changes resistance when bent. It will only change, the
resistance increases to 30- 40 kilo ohms at 90 degrees.
The sensor measures ¼ inch wide, 4-1/2 inches long
and 0.19 inches.
B.1. flex sensor 2.2:
Flex Sensor 2.2" RoHS Compliant
Description: A simple flex sensor 2.2" in length. As
the sensor is flexed, the resistance across the sensor
increases. The resistance of the flex sensor changes
when the metal pads are on the outside of the bend
(text on inside of bend). Connector is 0.1" spaced and
bread board friendly. Note: Please refrain from flexing
or straining this sensor at the base. The usable range of
the sensor can be flexed without a problem, but care
should be taken to minimize flexing outside of the
usable range. For best results, securely mount the base
and bottom portion and only allow the actual flex
sensor to flex. Fig.2 shows one type of flex sensor.
Working
The impedance buffer in the [Basic Flex
Sensor Circuit is a single sided operational amplifier,
used with these sensors because the low bias current
of the op amp reduces error due to source impedance
of the flex sensor as voltage divider.
In this two or three sensors are connected
serially and the output from the sensors is inputted to
the analog to digital converter in the controller. The
outputs from the flex sensors are inputted into
LM258/LM358 op-amps and used a non-inverted style
setup to amplify their voltage. The greater the degree
of bending the lower the output voltage. The output
voltage is determined based on the equation Vin *R1 /
(R1 + R2), where R1 is the other input resistor to the
non-inverting terminal. Using the voltage divider
concept the output voltage is determined and it ranges
from 1.35v to 2.5v.
B.2 Characteristics of flex sensors
Fig2. Flex sensor 2.2
Features
Angle Displacement Measurement
Bends and Flexes physically with motion
device
Possible Uses
Low profile
Simple construction
Applications
Robotics
Gaming (Virtual Motion)
Medical Devices
Specifications
Parameter
Life cycle
Height
Temperature range
Fig3. Bending VS Resistance
value
>1 million
<0.43mm
-35C to +80C
Fig4. Resistance VS Voltage
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International Conference on Global Trends in Engineering, Technology and Management (ICGTETM-2016)
C.PIC MICROCONTROL LER:
Programmable Brownout Reset
PIC microcontrollers are popular processors
developed by Microchip Technology with built-in
RAM, memory, internal bus, and peripherals that can
be used for many applications. PIC originally stood
for ―Programmable Intelligent Computer‖ but is now
generally regarded as a ―Peripheral Interface
Controller‖. PIC microcontrollers can be programmed
in Assembly, C or a combination of the two. Other
high-level Each PIC has unique features and subtle
differences. The correct choice for your project
depends on many factors: programming languages can
be used but embedded systems software is primarily
written in C. PIC microcontrollers are broken up into
two major categories: 8-bit microcontrollers and 16bit Microcontrollers.
Instruction Set
Packages
Yes
Yes
75
Instructions;
83 with
Extended
Instruction
Set Enabled
40-Pin PDIP
44-Pin QFN
44-Pin
TQFP
75
Instructions;
83 with
Extended
Instruction
Set Enabled
40-Pin PDIP
44-Pin QFN
44-Pin
TQFP
C.2.PIC18F4420/4520 controller
PICs also come in several types of packages:
Plastic Dual Inline Package (PDIP)
Small-Outline Transistor (SOT)
Dual Flat No-lead (DFN)
Mini Small Outline Package (MSOP)
Thin Quad Flat Pack (TQFP)
Plastic Leaded Chip Carrier (PLCC)
Ceramic QUAD pack (CERQUAD)
C.1 features of PIC18F4420/4520
Features
Operating Frequency
Program Memory
(Bytes)
Program Memory
(Instructions)
Data Memory (Bytes)
Data EEPROM
Memory (Bytes)
Interrupt Sources
I/O Ports
Timers
Capture/Compare/PW
M Modules
Enhanced
Capture/Compare/PW
M Modules
Serial Communications
Parallel
Communications (PSP)
10-Bit Analog-toDigital Module
Programmable
High/Low-Voltage
Detect
ISSN: 2231-5381
PIC18F442
0
DC – 40
MHz
PIC18F452
0
DC – 40
MHz
16384
32768
8192
16384
768
1536
256
256
20
Ports A, B,
C, D, E
4
20
Ports A, B,
C, D, E
4
III. CONCLUSION
In this paper, using various length of flex
sensor, zigbee transmitter and receiver and PIC
controller, gesture of deaf and mute person using sing
language effectively converted into audio and video
output. So that, normal person easily communicate
with deaf and mute person.
Conclude that difference of understanding of
deaf and mute person with compare normal person is
effectively reduced by using this flex sensor gloves
with the help of sign language.
1
1
REFERENCES
1
1
MSSP,
Enhanced
USART
MSSP,
Enhanced
USART
Yes
Yes
13 Input
Channels
13 Input
Channels
[3]
Yes
Yes
[4]
Fig5.PIC Controller
[1]
[2]
José Emiliano López-Noriega, Miguel Iván, ―Glove-Based
Sign
Language
Recognition
Solution
to
Assist
Communication for Deaf User‖ 11th International
Conference on Electrical Engineering, Computing Science
and Automatic Control (CCE),2014
Praveenkumar S Havalagi, Shruthi Urf Nivedita. ―The
amazing digital gloves that give voice to the Voiceless”
International Journal of Advances in Engineering &
Technology, ISSN: 2231-1963, Mar. 2013.
Hong Li and Michael Greenspan, ―Multi-scale Gesture
Recognition from Time-Varying Contours‖, Proceedings of
the Tenth IEEE International Conference on Computer V
2013.
Juan P. Wachs, Helman Stern and Yael Edan, ―Cluster
Labeling and Parameter Estimation for the Automated Setup
http://www.ijettjournal.org
Page 643
International Conference on Global Trends in Engineering, Technology and Management (ICGTETM-2016)
[5]
[6]
of a Hand-Gesture Recognition System”, IEEE Transactions
on Systems, March 2009
Hitoshi Hongo, Mitsunori Ohya, Mamoru face for
Recognition" Journal of Cognitive Yasumoto, Yoshinori
Niwa, and Kazuhiko Neuroscience, Vol-3, NO-1, 2008.
Sushmita Mitra , Tinku Acharya ―Gesture Recognition: A
Survey‖ IEEE TRANSACTIONS ON SYSTEMS, MAN, AND
CYBERNETICS—PART C: APPLICATIONS AND REVIEWS,
VOL. 37, NO. 3, MAY 2007
[7]
[8]
[9]
[10]
[11]
[12]
Man, and Toshiyuki Kirishima, Kosuke Sato and Kunihiro
Chihara, ―Real-Time Gesture Recognition by Learning and
Selective Control of Visual Interest Points”, IEEE
Transactions on Pattern Analysis and Machine Intelligence,
VOL. 27, NO. 3, MARCH 2005, pp. 351-364
Cybernetics—Part A: Systems And Humans, VOL. 35, NO.
6, NOVEMBER 2005, pp. 932-944.
C. Kwok, D. Fox, and M. Meila, ―Real-time particle filters,‖
Proc. IEEE, vol. 92, no. 3, pp. 469–484 March 2004.
R. Bowden, D. Windridge, T. Kadir, A. Zisserman, and M.
Brady, ―A
linguistic feature vector for the visual
interpretation of sign language,‖ in Proc. 8th Eur. Conf.
Comput. Vis., New York: Springer-Verlag, 2004, pp. 391–
401
H. S. Yoon, J. Soh, Y. J. Bae, and H. S. Yang, ―Hand gesture
recognition using combined features of location, angle and
velocity,‖ Pattern Recogn., vol. 34, pp. 1491–1501, 2001.
C. L. Lisetti and D. J. Schiano, ―Automatic classification of
single facial images,‖ Pragmatics Cogn., vol. 8, pp. 185–235,
2000
.
ISSN: 2231-5381
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