DESIGN AND MODELING GESTURE RECOGNITION OF HANDWRITTEN DIGIT USING ACCELEROMETER BASED DIGITAL PEN

International Engineering Journal For Research & Development

E-ISSN NO:-2349-0721

Vol.4 Issue 1

Impact factor : 3.35

DESIGN AND MODELING GESTURE RECOGNITION OF HANDWRITTEN DIGIT USING ACCELEROMETER BASED DIGITAL PEN 1

PROF. S. PAHUNE, 2 PHALGUNI KULURKAR, 3PRIYANKA FUNDE, 4SHILPA POTHARE ________________________________________________________________________________________ ABSTRACT 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 preprocessing 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. KEYWORDS: MEMS accelerometer, handwritten gesture recognition, trajectory algorithm.

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 air. The character recognition is done by an MEMS accelerometer. This accelerometer gives response 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 improve the recognition accuracy. (An efficient acceleration error compensation algorithm based on zero velocity compensation was developed to 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 accuracy. If the

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International Engineering Journal For Research & Development

Vol.4 Issue 1

orientation of the instrument was estimated precisely, the motion trajectories of the instrument were reconstructed accurately.

BRIEF LITERATURE SURVEY: Mr. Kunal J. Patil11, Mr. A. H. Karode2, Mr. S. R. Suralkar3 International Journal of Application or Innovation in Engineering & Management (IJAIEM) Web Site:www.ijaiem.org Email: editor@ijaiem.orgVolume 3, Issue 4, April 2014 ISSN 2319 – 4847 This paper Describe Accelerometer based 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 handwritten 2) S. T. Gandhe, Nikita A. Pawar, Mayuri S. Hingmire, Kalpesh P. Mahajan, Devshri V. PatilVolume 5, Issue 1, January 2015 ISSN: 2277 128X

International Journal of Advanced Research in Computer Science and

Software Engineering.

This paper describes the method for humans to interact with digital world and use the computer with just our hand movements. The paper is based on image processing. The camera detects gestures, and converts those gestures into equivalent digital algorithms as programmed. The complete computer can be controlled through hand gestures. This will make our over all work easier and efficient. 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 gestures.

3) Prachi A Deshpande1, Prajakta A Patil2 International Journal of Innovative Research in Computer and Communication Engineering (An ISO 3297: 2007 Certified Organization) Vol. 3, Issue 4, April 2015.

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 preprocessing 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.

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International Engineering Journal For Research & Development

Vol.4 Issue 1

PROBLEM FORMULATION As the world is modernized, the computers have become smart, but they still use the same keyboard and mouse. The power to sense what we want to say to a computer using optical devices is the purpose of this paper. The paper uses simple Web Cam as a sensing element to detect the gestures and hand movements.

The camera senses the color bands and gestures which are then converted into digital form by using algorithms. These algorithms are implemented using MATLAB programming.

OBJECTIVES The main objective of this paper is to have an intelligent computer, which can understand human gestures, and to introduce new technology, by which humans can interact with computer. 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.

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 built-in libraries (i.e. toolboxes) for Signal Processing, image processing, bio-matrix, data acquisition, etc.

The paper uses simple Web Cam as a sensing element to detect the gestures and hand movements.

The camera senses the color bands and gestures which are then converted into digital form by using algorithms.

These algorithms are implemented using MATLAB programming. In this paper, we mainly focus on Air writing (character recognition), mouse operations like its movement, left click and right click, drag and drop, snapshot. (a) Data-Glove based approaches: Data glove based interface are designed and researched for replacing static and fixed keyboard and mouse to have more natural way of communication as human being does by making gestures while communication. But have this, the gesture must be recognized first and thus data glove is used.

It provides data based on the angular measure of the bones in hand. Gestures are the first most interactive module for 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

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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 gestures started very early along with the development of the first wearable data gloves. There were many hurdles at that time in interpreting camera based gestures. Coupled with very low computing power available only on main frame computers, cameras offered very poor resolution along with colour inconsistency. The theoretical developments that lead to identifying skin segmentation were in its infancy and were not widely recognized for its good performance that we see today. Despite these hurdles, the first computer vision gesture recognition system was reported in 1980s. (c) Coloured markers approach: In this approach, the human hand used to wear marked gloves or coloured markers with some colours to direct the process of tracking the hand and locating the palm and fingers. It provides the ability to extract geometric features necessary to form hand shape .There might be different colours of colour glove, where three different colours are used to represent the fingers and palms, where a wool glove was used. The MIT-LED glove was developed at the MIT Media Laboratory in the early 1980s as part of a camera-based LED system to track body and limb position for real-time computer graphics animation. A camera sitting in front of the user could capture number of LEDs as 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 The portable device consists of a triaxial accelerometer microcontroller with a 10-b A/D converter, and a wireless transceiver. 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 transmits the acceleration signals wirelessly to a personal computer (PC). The output of any axis is analog voltage which is directly proportional to the acceleration in that axis.

Acceleration values can be positive, negative or zero. So, the output voltage has a zero bias output. The output given at this point means zero acceleration in that particular axis. So, the zero point voltage is greater than output voltage, it indicates the negative acceleration. The accelerometer works with three modes, they are Standby mode, auto sleep mode and Low power mode. The microcontroller integrates a high-performance 10bit A/D converter and 8-b microcontroller unit (MCU) on a signal chip. The output signals of the accelerometer are sampled at 100 Hz by the 10-bit A/D converter. Then, all the data sensed by MEMS are transmitted to PC wirelessly by an RF transceiver, at 2.4- GHz transmission band with 1-Mb/s transmission rate. The overall power consumption of the digital pen circuit is 30 mA at 3.7 V.

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Figure1: Block diagram of proposed system This system we are using triaxial-accelerometer, a microcontroller (with A/D converter), and a 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 transmits the acceleration signals wirelessly to a personal computer (PC).The acceleration signals measured from the triaxial accelerometer are transmitted to computer via the wireless module.

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[8] Miss Leenamahajan ,Prof G,.A.Kulkarni ,” Digital Pen for Handwritten Digit and Gesture Recognition Using Trajectory Recognition Algorithm Based On Triaxial Accelerometer- A Review

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