Vol.4 Issue 1
International Engineering Journal For Research & Development
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E-ISSN NO:-2349-0721
A REVIEW PAPER ON: DESIGN AND MODELING GESTURE RECOGNITION OF HANDWRITTEN ALPHANUMERIC USING ACCELEROMETER BASED DIGITAL PEN Mr. Amit Fulsunge (Assistant Professor) Ms.Swetali D. Pawade Dept. of Electronics & Communication Engg. Dept.of Electronics & Communication Engg. Email: -Amitfulsunge@gmail.com Email:-swetalipawade26@gmail.com _________________________________________________________________________________________ air. The character recognition is done by an MEMS
ABSTRACT
accelerometer. This accelerometer gives response In this paper an MEMS accelerometer mostly based on gesture recognition algorithm and its application are presented. The hardware module
consists
of
a
triaxial
MEMS
accelerometer, microcontroller, and RF wireless transmission module for sensing and collecting accelerations of handwriting and hand gesture trajectories. Users will use this digital pen to write down digits, alphabets & some hand gestures. The accelerations of hand motions deliberate by the accelerometer are transmit wirelessly to a personal computer for trajectory recognition. The trajectory algorithm collected of information variety assortment, signal pre-processing for reconstruct the trajectory to satisfy the collective errors caused by drift of sensors. So, by altering the location of MEMS (micro electro mechanical systems) we can proficient to demonstrate the alphabetical characters and numerical on PC.
for every slight deflection or movement in the system. Accelerometer is developed by using MEMS technology. A significant advantage of accelerometer for general motion sensing is that they can be operated without any external reference and limitation in working conditions. However, gesture
recognition
is relatively complicated
because different users have different speeds and styles to generate various motion trajectories. Thus, many researchers have tried to increasing the accuracy of handwriting recognition systems. Recently, some researchers have working on reducing the error of handwriting trajectory reconstruction by using acceleration signals and angular velocities of inertial sensors. However, the reconstructed trajectories suffer from various intrinsic errors of inertial sensors. Hence, many researchers have focused on developing effective algorithms to reduce error of inertial sensors & to
KEYWORDS: MEMS accelerometer, handwritten
improve the recognition accuracy. (An efficient
gesture recognition, trajectory algorithm.
acceleration error compensation algorithm based on zero velocity compensation was developed to
INTRODUCTION Handwriting Recognition is mostly used for security & authentication purpose. There are two types of recognition offline recognition & online recognition. The constructed system is an online hand writing character recognition written in
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reduce acceleration errors for acquiring accurate reconstructed trajectory. The features of the preprocessed acceleration signals of each axis include ZCD and Range. Before classifying the hand
motion
trajectories,
we
perform
the
procedures of feature selection and extraction methods. The PNN classifier is used to get better
1
Vol.4 Issue 1
International Engineering Journal For Research & Development accuracy. If the orientation of the instrument was
equivalent digital algorithms as programmed. The
estimated precisely, the motion trajectories of the
complete computer can be controlled through hand
instrument were reconstructed accurately.
gestures. This will make our over all work easier and efficient. This paper deals with the controlling
BRIEF LITERATURE SURVEY:
all operations of mouse such as right click, left
Mr. Kunal J. Patil11, Mr. A. H. Karode2, Mr. S. R. Suralkar3 International Journal of Application or Innovation
in
(IJAIEM)
Web
Engineering
&
Management
Site:www.ijaiem.org
Email:
editor@ijaiem.orgVolume 3, Issue 4, April 2014
click and movement of cursor over the desktop, drag and drop, snapshot, Air writing and painting through hand gestures. 3) Prachi A Deshpande1, Prajakta A Patil2 International Journal of Innovative Research in Computer and Communication Engineering (An
ISSN 2319 – 4847
ISO 3297: 2007 Certified Organization) Vol. 3, This paper Describe Accelerometer based
Issue 4, April 2015.
gesture recognition method for Digit Recognition. Accelerometer based gesture recognition is one of the widely implemented method in the recognition scenario. We have implemented a 3D input digital pen which works on triaxial accelerometer to sense human gesture. This digital pen embedded with triaxial accelerometer, microcontroller, RF wireless transmitter module. The triaxial accelerometer measure acceleration signal along all the 3 axis. Accelerated signal process through microcontroller and serially transmitted through RF transmitter which can be received at remote place RF receiver. With the help of MATLAB tool feature vector are generated from received accelerated signal using zero crossing detector(ZCD) & range to recognize
In this paper an MEMS accelerometer mostly based on gesture recognition algorithm and its application are presented. The hardware module consists of a triaxial MEMS accelerometer, microcontroller, and RF wireless transmission module for sensing and collecting accelerations of handwriting and hand gesture trajectories. Users will use this digital pen to write down digits, alphabets& some hand gestures. The accelerations of hand motions deliberate by the accelerometer are transmit wirelessly to a personal computer for trajectory recognition. The trajectory algorithm collected of information variety assortment, signal pre-processing for reconstruct the trajectory to satisfy the collective errors caused by drift of
handwritten
sensors. So, by altering the location of MEMS 2) S. T. Gandhe, Nikita A. Pawar, Mayuri S.
(micro electro mechanical systems) we can
Hingmire, Kalpesh P. Mahajan, Devshri V.
proficient
PatilVolume 5, Issue 1, January 2015 ISSN: 2277
characters and numerical on PC.
to
demonstrate
the
alphabetical
128X International Journal of Advanced Research in Computer Science and Software Engineering. This paper describes the method for
PROBLEM FORMULATION As
the
world
is
modernized,
the
humans to interact with digital world and use the
computers have become smart, but they still use the
computer with just our hand movements. The paper
same keyboard and mouse.
is based on image processing. The camera detects gestures,
and
converts
those
gestures
into
The power to sense what we want to say to a computer using optical devices is the purpose
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2
Vol.4 Issue 1
International Engineering Journal For Research & Development of this paper. The paper uses simple Web Cam as a
The paper uses simple Web Cam as a sensing
sensing element to detect the gestures and hand
element
movements.
movements. The camera senses the color bands and
to
detect
the
gestures
and
hand
gestures which are then converted into digital form The camera senses the color bands and gestures which are then converted into digital form by using algorithms.
by
using
algorithms.
These
algorithms
are
implemented using MATLAB programming. In this paper, we mainly focus on Air writing
These algorithms are implemented using
(character recognition), mouse operations like its movement, left click and right click, drag and drop,
MATLAB programming.
snapshot. OBJECTIVES (a) Data-Glove based approaches: Data glove based The main objective of this paper is to have
interface are designed and researched for replacing
an intelligent computer, which can understand
static and fixed keyboard and mouse to have more
human gestures, and to introduce new technology,
natural way of communication as human being
by which humans can interact with computer. This
does by making gestures while communication.
paper deals with the controlling all operations of
But have this, the gesture must be recognized first
mouse such as right click, left click and movement
and thus data glove is used. It provides data based
of cursor over the desktop, drag and drop, snapshot,
on the angular measure of the bones in hand.
Air writing and painting through hand gesture.
Gestures are the first most interactive module for
This paper deals with the controlling all operations of mouse such as right click, left click and movement of cursor over the desktop, drag and drop, snapshot, Air writing and painting through hand gesture. MATLAB is a numerical computing environment,
developed
by
Math
Works.
MATLAB allows the generation, manipulation and processing of signals and images in the form of arrays and Matrices. It contains hundreds of builtin libraries (i.e. toolboxes) for Signal Processing, image processing, bio-matrix, data acquisition, etc.
game control, Wilmot. Komura and Lam have proposed a method to control a small robot walking motion using the data glove. They actually proposed a mapping system between finger motion and 3D characters location. In this proposed paper, we are mapping the finger motion with the 3D mouse pointer to sketch something useful on the computer screen. Basically the mapping is between the real world and the digital world, connecting each other. The data glove used for the experiment is an electronic device with motion
capture
sensors,
i.e.,
flex
sensors,
capturing the movements of each individual finger from physical world and converts them to digital signal using analog-to-digital convertor. This digital signal is then passed to the computer to further process and paints the digital or virtual world, as it is the mimic of physical or real world. (b) Vision
based
approaches:
In
vision
based
approach, Using cameras to recognize hand
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3
Vol.4 Issue 1
International Engineering Journal For Research & Development gestures started very early along with the
converter, and a wireless transceiver. The triaxial
development of the first wearable data gloves.
accelerometer measures the acceleration signals
There were many hurdles at that time in
generated
interpreting camera based gestures. Coupled with
microcontroller collects the analog acceleration
very low computing power available only on main
signals and converts the signals to digital ones via
frame computers, cameras offered very poor
the A/D converter. The wireless transceiver
resolution along with colour inconsistency. The
transmits the acceleration signals wirelessly to a
theoretical developments that lead to identifying
personal computer (PC). The output of any axis is
skin segmentation were in its infancy and were not
analog voltage which is directly proportional to the
widely recognized for its good performance that
acceleration in that axis. Acceleration values can be
we see today. Despite these hurdles, the first
positive, negative or zero. So, the output voltage
computer vision gesture recognition system was
has a zero bias output. The output given at this
reported in 1980s. (c) Coloured markers approach:
point means zero acceleration in that particularaxis.
In this approach, the human hand used to wear
So, the zero point voltage is greater than output
marked gloves or coloured markers with some
voltage, it indicates the negative acceleration. The
colours to direct the process of tracking the hand
accelerometer works with three modes, they are
and locating the palm and fingers. It provides the
Standby mode, auto sleep mode and Low power
ability to extract geometric features necessary to
mode. The microcontroller integrates a high-
form hand shape .There might be different colours
performance
of colour glove, where three different colours are
microcontroller unit (MCU) on a signal chip. The
used to represent the fingers and palms, where a
output signals of the accelerometer are sampled at
wool glove was used. The MIT-LED glove was
100 Hz by the 10-bit A/D converter. Then, all the
developed at the MIT Media Laboratory in the
data sensed by MEMS are transmitted to PC
early 1980s as part of a camera-based LED system
wirelessly by an RF transceiver, at 2.4- GHz
to track body and limb position for real-time
transmission band with 1-Mb/s transmission rate.
computer graphics animation. A camera sitting in
The overall power consumption of the digital pen
front of the user could capture number of LEDs as
circuit is 30 mA at 3.7 V.
by
a
user’s
10-bit
A/D
hand
motions.
converter
and
The
8-b
they were studded on the glove. The different illumination patterns for different gestures that could be interpreted by a computer. However, the performance was poor due to occlusions and the variations of any gesture performed by different users. One of the first instances of gesture recognition using a glove with finger tip markers was reported by Davies et al.
PROPOSED SYSTEM
Figure1: Block diagram of proposed system
The portable device consists of a triaxial accelerometer microcontroller with a 10-b A/D
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International Engineering Journal For Research & Development
Vol.4 Issue 1
This system we are using triaxial-accelerometer, a
reality,” IEEE Trans. Ind. Electron., vol. 54, no. 4,
microcontroller (with A/D converter), and a
pp. 2174–2189, Aug. 2007.
wireless
transceiver
(RF).
The
triaxial
accelerometer measures the acceleration signals generated
by
a
user’s
hand
motions.
The
microcontroller collects the analog acceleration Signals and converts the signals to digital ones via the A/D converter. The wireless transceiver
[6] E. Sato, T. Yamaguchi, and F. Harashima, “Natural interface using pointing behavior for human–robot gestural interaction,” IEEE Trans. Ind. Electron., vol. 54, no. 2, pp. 1105–1112, Apr. 2007.
transmits the acceleration signals wirelessly to a
[7] Y. S. Kim, B. S. Soh, and S.-G. Lee, “A new
personal computer (PC).The acceleration signals
wearable input device: SCURRY,” IEEE Trans.
measured from the triaxial accelerometer are
Ind. Electron., vol. 52, no. 6, pp. 1490–1499, Dec.
transmitted to computer via the wireless module.
2005.
Bibliography:
[8] Miss Leenamahajan ,Prof G,.A.Kulkarni ,”
[1] Jeen-Shing Wang and Fang-Chen Chuang, “An Accelerometer-Based
Digital
Pen
With
a
Trajectory Recognition Algorithm for Handwritten
Digital Pen for Handwritten Digit and Gesture Recognition
Using
Trajectory
Recognition
Algorithm Based On Triaxial Accelerometer.
Digit and Gesture Recognition,” IEEE Sens. J., vol. 59, no. 07, pp. 1543–1551, July. 2012. [2] Z. Dong, U. C. Wejinya, and W. J. Li, “An optical-tracking calibration method for MEMSbased digital writing instrument,” IEEE Sens. J., vol. 10, no. 10, pp. 1543–1551, Oct. 2010. [3] J. S.Wang, Y. L. Hsu, and J. N. Liu, “An inertial-measurement-unit-based trajectory
reconstruction
pen
algorithm
with
a
and
its
applications,” IEEE Trans. Ind. Electron., vol. 57, no. 10, pp. 3508–3521, Oct. 2010. [4] S.-H. P. Won, W. W. Melek, and F. Golnaraghi, “A Kalman/particle
filter-based position and
orientation estimation method using a position sensor/inertial measurement unit hybrid system,” IEEE Trans. Ind. Electron., vol. 57, no. 5, pp. 1787–1798, May 2010. [5] A. D. Cheok, Y. Qiu, K. Xu, and K. G. Kumar, “Combined
wireless
hardware
and
real-time
computer vision interface for tangible mixed
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