2. Abstract:
This paper describes a SMART INTERPRETERGLOVE to impart an
easier means of communication between
impaired Or deaf people and normal people using wireless data
transmission
A smart glove is incorporated with flex sensors whose,
resistance value changes according to the gesture specified by
the user. This gesture information is processed by the Arduino
Mega 2560 microcontroller and corresponding voice output is
given through controlling various devices or appliances and alert
messages based on pulse sensor data to indicate health issue of old
or disable person
3. Introduction
In the present world it is very complicated for the deaf & dumb people to talk with the
ordinary people as impaired people lacks the amenities which a normal person should
own. It actually becomes the same problem of two persons which knows two different
language, no one of them knows any common language so its becomes a problem to talk
with each other and so they requires a translator physically which may not be always
convenient to arrange and this same kind of problem occurs in between the Normal
Person and the Deaf person or the Normal Person and the Dumb person.
Hand movement data acquisition is used in many engineering applications ranging from
the analysis of gestures to the biomedical sciences. Glove-based systems represent one
of the most important efforts aimed at acquiring hand movement data. While they have
been around for over three decades, they keep attracting the interest of researchers
from increasingly diverse fields. The development of the most popular devices for hand
movement acquisition, glove-based systems, started about 30 years ago and continues to
engage a growing number of researchers. We choose to study the glove systems for sign
language understanding.
4. Objective
We are working on this project namely Hand talk 2 that translates hand gestures
into a particular message. Flex Sensors fixed with the glove pick up the gesture
made by the individual and then with the help of the Arduino that analog input is
converted to digital for various gestures. For every particular gesture, there is
specific digital output and that digital output for the specific gesture is reserved
for a specific message.
So, when the individual does that particular gesture, the predefined message for
that gesture is displayed on the LCD along with a beep (sound) and that specific
message is also transmitted in the form of text or call with the help of GSM module
to the various registered numbers.
5. Literature survey
[1] Subhankar Chattoraj et al., [1] have introduced a hand gesture recognition system to
recognize the different gesture made by deaf person to communicate using scale-
invariant
feature transform (SFIT) algorithm that bridges the gap between deaf and dumb people
and normal public. This system will focus on hand gesture recognition and development
of human computer interface (HCI) system which will achieve accuracy, real time
implementation of gesture processing. Here, the light intensity plays a key role in the
precision of the result, more light intensity causes
blurring of the image and affects the output result. This can be further developed by
implementing the database and speech conversion.
[2] Abhinandan Das et al., [2] here an effort has been made to develop a smart glove using
Intel Galileo Gen 2 lOT kit for real-time gesture recognition. The custom built glove uses
flex-sensors that are used to measure how much each finger is bent, deformed and the
MPU-6050 gyroscope that provides the yaw, pitch and roll values, and both orientation
and rotational movement details of the hand. These sensors are read and discretized
using the Intel Galileo Gen2 and Arduino microcontrollers. Then serial data is sent to a PC,
where the flex and orientation data are processed using a python script. The received data
is used to classify each gesture into the corresponding letter c1ass/ word class.
6. [3] S Yarisha Heera et al., [3] in this proposed system, sensors are incorporated on a glove
to detect the gestures and converted it to speech with the help of a Bluetooth module
and an Android Smart Phone. This will help in producing artificial speech which provides
an environment similar to daily communication which is hard to achieve for speech
impaired people. Flex sensors and MPU6050 are used to determine the gesture and
once the corresponding values of the accelerometer and gyroscope along with the flex
sensors are obtained, with the help of a Bluetooth module, the readings are sent to an
Android application. The Android application is accompanied with SQLite database
that consists of database of words in the Indian Sign Language. The readings obtained
from the sensors are mapped into the corresponding words with the help of the
database. Once the match is found for the reading, the output is produced in the
form of speech. This work could have been extended by using human voice for
communicating and by using alternative device for bluetooth as it has very short
range.
[4] M.S. Kasar et al., [4] in this approach, flex sensors are fitted on glove to sense
the finger movements. According to the finger movement made, ATmega328
microcontroller will display message on LCD. Sensor based recognition technique.
Development of a module which converts finger movement to sound using
embedded system. The text message is converted into voice using speak jet and
amplifier and voice is heard via speaker. In future it can be used in application like
remote handling, smooth traffic control.
7. Problem statement
This is the modified version of my first project Hand talk. HANDTALK 2 needs to be worn on the hand
by the needy and depending on the variation of the movements the device will convert it
intelligently into beep (can be voice too) and in a text. This message will be out from the beeper
and LCD display. The HANDTALK glove senses the movements through the flex sensors which detects
the different patterns of motion. The device can sense carefully each resistance and each
movement by the hand. Currently, the device can convert only a few, but depending on the success
of this device few more additional features may be added later onto this expressive system.
Technology has always been of great help to the disabled and given them a helping hand to allow
them to live a normal and healthy life like others.
8. Problems faced by the disable person regarding employment can be overcome by
our method. So in the implemented work an intelligent microcontroller based
system using Flex sensors is developed which is able to-
Convert gesture into voice and text.
Help a person to control his home appliances if he could not walk to
switchboard.
In today’s technology wireless gloves are not yet reliable because to be used as
wireless, the gloves should have inbuilt battery and some electronics controller
board which makes gloves heavier and may cause irritation. Thus wired
equipment’s are preferred for patients and partial disable people.
9. Proposed system
The work of this project start from movement of hand gloves where the flex sensors are
attached, and the value of sensor changes when its experiences the bending. The flex sensor is
another type of potentiometer are attach to the fingers when we bend the figure the value of
the sensor get changes. The changing value of the sensor is depend upon the resistance and
applied angle of the bending when we bend the sensor at some particular angle we can see the
value of the resistance is increase and accordingly the output get reduced. On the other way
we can say that it’s like a inversely proportional when the resistance of the sensor is increase
at that instant the value of output decrease and accordingly we can make project by getting
the advantage of this process.
After looking at the changing value of the output, the value of the get recorded
by the Arduino and show from the display attached to it. Here the process gets started the
Arduino gets different value from the sensor. The output value we can continuously see from
the LCD which attached to it.
11. Components & Specifications
Arduino UNO
Microcontroller ATmega328
Operating Voltage 5V
Input Voltage (recommended) 7-12V
Digital I/O Pins 14 (of which 6 provide PWM output)
Analog Input Pins 6
SRAM 2 KB (ATmega328)
EEPROM 1 KB (ATmega328)
DC Current per I/O Pin 40 mA
12. Flex sensors
•Operating voltage of FLEX SENSOR: 0-5V
•Can operate on LOW voltages
•Power rating : 0.5Watt (continuous), 1 Watt (peak)
•Life: 1 million
•Operating temperature: -45ºC to +80ºC
•Flat Resistance: 25K Ω
•Resistance Tolerance: ±30%
•Bend Resistance Range: 45K to 125K Ohms(depending on
bend)
13. Pulse sensor
•Biometric Pulse Rate or Heart Rate detecting sensor
•Plug and Play type sensor
•Operating Voltage: +5V or +3.3V
•Current Consumption: 4mA
•Inbuilt Amplification and Noise cancellation circuit.
•Diameter: 0.625”
•Thickness: 0.125” Thick
14. LCD display
•Operating Voltage is 4.7V to 5.3V
•Current consumption is 1mA without backlight
•Alphanumeric LCD display module, meaning can display
alphabets and numbers
•Consists of two rows and each row can print 16
characters.
•Each character is build by a 5×8 pixel box
•Can work on both 8-bit and 4-bit mode
•It can also display any custom generated characters
•Available in Green and Blue Backlight
21. Advantages
Easy to operate: Anyone can operate it easily.
Easy to define gestures: we can add or define our own gestures.
Communication is possible in any language.
It requires fewer components so its cost is low.
Small in size and Light weight.
Flexible to users.
It takes less power to operate system
Reliable and helpful to disable people
22. APPLICATIONS
Useful for physically challenged people.
Provides easy communication between the impaired people and the normal
people.
Wireless communication area
Wireless data transmission area.
In Hospitals
Old age orphanage houses
Useful for ill old people staying alone to send message in emergency
Useful for disable people to handle home appliances easily without any help
23. Conclusion
A Smart Speaking Glove for Speech impaired People is designed and implemented with
four gestures. Each gesture specifies basic needs such as “NEED WATER”, “NEED
MEDICINE”, “NEED FOOD”, “TURN ON TV”. This system is more reliable, efficient, and
easy to use and a light weight solution to the user as compared to other proposed
systems.
This bridges the communication gap between impaired, physically challenged people
and others. During this project we have faced various challenges and we have tried to
minimize the problem.
24. References
[1] Subhankar Chattoraj, Karan Vishwakarma, Tanmay Paul,“Assistive System for Physically Disabled People using
Gesture Recognition”, IEEE 2nd International
Conference on Signal and Image Processing, 2017
[2] Abhinandan Das, Lavish Yadav, Mayank Singhal, Raman Sachan, Hemang Goyal, “Smart Glove for Sign
Language Communications”, vol. 5, Issue 3, pp. 1589-1594, March 2016
[3] S Yarisha Heera, Madhuri K Murthy, Sravanti V S, Sanket Salvi, “Talking Hands – An Indian Sign Language to
Speech Translating Gloves”, ICIMIA, pp. 746-751, 2017
[4] M.S.Kasar, AnvitaDeshmukh, Akshada Gavande Priyanka Ghadage, “Smart Speaking Glove-Virtual tongue for
Deaf and Dumb”, IJAREEIE, Vol. 5, Issue 3, pp. 1588-1594, 2016
[5] Sakunthala Vegunta, A.B.Bhavani, M.Mallikarjun, “Gesture Based Sensor Device with GPS and GSM Technology
for Dumb People” ,ICCES, 2017
[6] AbjhijtAuti, V. G. Puranik, Dr. A. K. Kureshi, “Speaking Gloves for Speechless Persons”, International Journal of
Innovative Research in Science, Engineering and
Technology, Volume 3, Special Issue 4, pp. 282-290, April 2014
