Project handtalk
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This document summarizes a presentation on HandTalk, a technology that aims to help deaf and mute individuals communicate. HandTalk uses a virtual reality glove called the P5 glove that detects finger gestures and converts them to text using gesture recognition software. The text is then converted to speech so hearing individuals can understand the deaf or mute person. The goal is to create an accurate and inexpensive alternative to existing expensive gesture recognition systems. The presentation outlines the hardware, software, user interface, motivation, design, problems with other systems, and future enhancements of the HandTalk system.
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- The Android app receives character data from the Bluetooth, translates the characters into words, and displays and speaks the words in both Arabic and English.
- The total estimated cost of materials for the glove prototype is $70, using low-cost components like an Arduino, Bluetooth module, and copper sensors to keep costs minimal.
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1) A data glove with bend sensors to detect finger movements
2) An accelerometer-based tilt detector to sense hand tilting
3) A gesture detector module that analyzes the bend and tilt data and identifies gestures
4) A speech synthesizer and speaker controlled by the microcontroller to vocalize the recognized gestures
5) An LCD display to show the gesture text for the deaf. The system uses microcontrollers, sensors, and other circuits to recognize static finger gestures and hand tilts and convert them into synthesized speech or displayed text.
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Survey Paper on Raspberry pi based Assistive Device for Communication between...
This document discusses several research papers on developing assistive devices to aid communication between blind, deaf, and mute individuals. It begins with an abstract describing the goal of converting sign language to voice and text using a Raspberry Pi, camera, speaker and LCD display by recognizing human gestures. It then summarizes 8 research papers on related topics, describing systems that use flex sensors on gloves to detect sign language and translate it to speech/text, or use image processing on hand gestures. The document concludes by outlining the proposed methodology, hardware and software requirements, and potential limitations and future work for a sign language translation system using a Raspberry Pi, camera and sensors.
Hand Gesture Vocalizer
Digital voice over is a social project aimed at improving the ability of speaking and hearing by enabling people to communicate better with the public. There are approximately 9.1 billion deaf and hard of hearing people worldwide. They encounter many problems while trying to communicate with the society in daily life. Deaf and speech impaired people often use language to communicate but have difficulty communicating with people who do not understand the language. Sign language uses sign language patterns i.e., body language, gestures and movements of arms and fingers etc. to convey information about people. relies on. This project was designed to meet the need to create electronic devices that can translate sign language into speech to facilitate communication between the deaf and dumb and the public. Venkat P. Patil | Suyash Mali | Girish Ghadi | Chintamani Satpute | Amey Deshmukh "Hand Gesture Vocalizer" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-7 | Issue-2 , April 2023, URL: https://www.ijtsrd.com.com/papers/ijtsrd55157.pdf Paper URL: https://www.ijtsrd.com.com/engineering/electronics-and-communication-engineering/55157/hand-gesture-vocalizer/venkat-p-patil
A Translation Device for the Vision Based Sign Language
The Sign language is very important for people who have hearing and speaking deficiency generally called Deaf and Mute. It is the only mode of communication for such people to convey their messages and it becomes very important for people to understand their language. This paper proposes the method or algorithm for an application which would help in recognizing the different signs which is called Indian Sign Language. The images are of the palm side of right and left hand and are loaded at runtime. The method has been developed with respect to single user. The real time images will be captured first and then stored in directory and on recently captured image and feature extraction will take place to identify which sign has been articulated by the user through SIFT(scale invariance Fourier transform) algorithm. The comparisons will be performed in arrears and then after comparison the result will be produced in accordance through matched key points from the input image to the image stored for a specific letter already in the directory or the database the outputs for the following can be seen in below sections. There are 26 signs in Indian Sign Language corresponding to each alphabet out which the proposed algorithm provided with 95% accurate results for 9 alphabets with their images captured at every possible angle and distance.
Artificial Intelligence for Speech Recognition
Speech recognition software uses artificial intelligence techniques to transform spoken words into text. It has various applications, such as legal and medical transcription. Automatic speech recognition involves mapping acoustic speech signals to text. However, speech recognition also faces technical challenges, such as differentiating words in continuous speech and accounting for variations in accents and pronunciations. The document discusses the history and various applications of speech recognition technology.
Two-Column Newsletter
The document discusses several emerging technologies that aim to create more natural interfaces between humans and computers. These include speech recognition technologies that allow users to control computers with voice commands instead of keyboards and mice. It also discusses advances in natural language processing that could allow users to communicate with computers in human languages rather than programming languages. The document also briefly outlines virtual reality technologies that immerse users in simulated environments and mental interface technologies that aim to interpret users' brain activity as commands.
Glove based wearable devices for sign language-GloSign
Loss of the capability to talk or hear has psychological and social effects on the affected individuals due to the absence of appropriate interaction. Sign Language is used by such individuals to assist them in communicating with each other. This paper proposes a glove called GloSign that can convert American sign language to characters. This glove consists of flex and inertial measurement unit (IMU) sensors to identify gestures. The data from glove is uploaded on IoT platform, which makes the glove portable and wireless. The data from gloves is passed through a k-nearest neighbors (KNN) Algorithm machine learning algorithm to improve the accuracy of the system. The system was able to achieve an accuracy of 96.8%. The glove can also be used to form sentences. The output is displayed on the screen or is converted to speech. This glove can be used in communicating with people who don’t know sign language.
AlterEgo: A Personalized Wearable Silent Speech Interface
AlterEgo: A Personalized Wearable Silent Speech Interface. MIT
Arnav Kapur
MIT Media Lab
Cambridge, USA
arnavk@media.mit.edu
Shreyas Kapur
MIT Media Lab
Cambridge, USA
shreyask@mit.edu
Pattie Maes
MIT Media Lab
Cambridge, USA
pattie@media.mit.edu
HCI BASED APPLICATION FOR PLAYING COMPUTER GAMES | J4RV4I1014
This paper describes a command interface for games based on hand gestures and voice command defined by postures, movement and location. The system uses computer vision requiring no sensors or markers by the user. In voice command the speech recognizer, recognize the input from the user. It stores and passes command to the game, action takes place. We propose a simple architecture for performing real time colour detection and motion tracking using a webcam. The next step is to track the motion of the specified colours and the resulting actions are given as input commands to the system. We specify blue colour for motion tracking and green colour for mouse pointer. The speech recognition is the process of automatically recognizing a certain word spoken by a particular speaker based on individual information included in speech waves. This application will help in reduction in hardware requirement and can be implemented in other electronic devices also.
Gesture Vocalizer
Generally dumb people use sign language for communication but they find difficulty in communicating with others who don’t understand sign language. This project aims to lower this barrier in communication. It is based on the need of developing an electronic device that can translate sign language into speech in order to make the communication take place between the mute communities with the general public possible. A Wireless data gloves is used which is normal cloth driving gloves fitted with flex sensors along the length of each finger and the thumb. Mute people can use the gloves to perform hand gesture and it will be converted into speech so that normal people can understand their expression. Sign language is the language used by mute people and it is a communication skill that uses gestures instead of sound to convey meaning simultaneously combining hand shapes, orientations and movement of the hands, arms or body and facial expressions to express fluidly a speaker’s thoughts. Signs are used to communicate words and sentences to audience.
IRJET - Sign to Speech Smart Glove
This document describes a smart glove system that translates sign language gestures into speech and text to help deaf and mute people communicate. The system uses flex sensors on a glove to detect hand gestures, which are processed by an Arduino microcontroller. The Arduino identifies letters and words from the gestures and outputs them as speech from a connected speaker and as text on an Android phone app. The goal is to help deaf-mute individuals effectively convey information to people without sign language training by translating their gestures into audio and text in real-time.
Electronic Hand Glove Through Gestures For Verbally Challenged Persons
This paper presents design of Electronic hand glove to facilitate an easy and better communication through
synthesized speech for the verbally challenged peoples. Most Probably, a speechless person communicates
through sign language which is not understood by the majority of people. The proposed system is designed to
solve this problem. Gestures of fingers of a person of this glove will be converted into synthesized speech to
convey an audible message to others. Speech is typically accompanied by manual gestures. Earlier there were
many systems designed for dumb and deaf to interact with ordinary people. But these systems had many
drawbacks and interrupts. We are designing such a system that even a dumb, deaf and blind can communicate
with each other without taking help of ordinary people. This system is going to help them to interact with the
outside world.
183
Touch is one of the most common forms of sign language used in oral communication. It is most commonly used by deaf and dumb people who have difficulty hearing or speaking. Communication between them or ordinary people. Various sign-language programs have been developed by many manufacturers around the world, but they are relatively flexible and affordable for end users. Therefore, this paper has presented software that introduces a type of system that can automatically detect sign language to help deaf and mute people communicate better with other people or ordinary people. Pattern recognition and hand recognition are developing fields of research. Being an integral part of meaningless hand-to-hand communication plays a major role in our daily lives. The handwriting system gives us a new, natural, easy-to-use communication system with a computer that is very common to humans. Considering the similarity of the human condition with four fingers and one thumb, the software aims to introduce a real-time hand recognition system based on the acquisition of some of the structural features such as position, mass centroid, finger position, thumb instead of raised or folded finger.
Review Paper on Two Way Communication System with Binary Code Medium for Peop...
1) The document discusses a proposed system to aid communication between blind, deaf, and mute individuals using binary code.
2) It reviews existing research on communication systems using Morse code and tactile methods.
3) The proposed system would convert speech to visual contexts and vibrations, and vice versa, using a multimodal approach to allow communication across disabilities.
gesture-recognition
This document is a final report on gesture recognition submitted by three students. It contains an abstract, introduction, background information on gesture recognition including American Sign Language and object recognition techniques. It discusses digital image processing and neural networks. It outlines the approach, modules, flowcharts, results and conclusions of the project, which developed a method to recognize static hand gestures using a perceptron neural network trained on orientation histograms of the input images. Source code and applications are also discussed.
Procedia Computer Science 94 ( 2016 ) 295 – 301 Avail.docx
Procedia Computer Science 94 ( 2016 ) 295 – 301
Available online at www.sciencedirect.com
1877-0509 © 2016 Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
Peer-review under responsibility of the Conference Program Chairs
doi: 10.1016/j.procs.2016.08.044
ScienceDirect
The 11th International Conference on Future Networks and Communications
(FNC 2016)
Assistive Technology for Deaf People Based on Android
Platform
Emad E. Abdallaha,* Ebaa Fayyoumi b
a,bFaculty of Information Technology, Hashemite University, Zarqa 13115, Jordan
Abstract
Social communication is one of the most important pillars that our society based on. It is well-known that the language is the
only way to communicate and interact with each other verbally or non-verbal way. People with special needs are members of
this society and have the right to enjoy the communication with the external environment in an easy and professional manner.
This paper aims to provide an interesting application that guarantees ultimate communication with the disabled users and vice
versa. The key feature of this application is employing the Arabic language as a medium of communication to learn all the sign
language terms.
The power of this application appears in two aspects: first of all, the ability of normal people to communicate with the targeted
people without having any previous knowledge on signs language. This can be either achieved by voice recognition of words
or by typing the words in the Arabic language. The application is then displays the appropriate image(s) in the sign language.
Secondly, and more importantly, people with special needs communicate with normal people by choosing the signs images on
their phones from the numerous categories stored in the databases which express their ideas and thoughts. Consequently, the
set of images is transformed into a text paragraph. We evaluated our application by testing it on real deaf and dumb users. We
carefully created scenarios on realistic situations. The early results are promising as all deaf found the proposed technology
useful and 90% of them wanted to use it on daily basis.
© 2016 The Authors. Published by Elsevier B.V.
Peer-review under responsibility of the Conference Program Chairs.
Keywords: Disabled people; deaf; assistive technology; sign language; mobile application.
* Corresponding author. Tel.: +0096-795673231; fax: +00962(05) 3826625.
E-mail address: [email protected]
© 2016 Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
Peer-review under responsibility of the Conference Program Chairs
296 Emad E. Abdallah and Ebaa Fayyoumi / Procedia Computer Science 94 ( 2016 ) 295 – 301
1. Introduction
Deafness and hearing loss is the condition of incapability to h ...
Finger reader
The document describes a finger-worn device called the FingerReader that assists visually impaired users in reading printed text. The FingerReader uses a small camera mounted on a 3D printed ring to scan and track text lines. It provides tactile and auditory feedback to help users smoothly track lines of text. The device aims to give visually impaired people more independence and access to printed materials than existing assistive technologies.
Wireless and uninstrumented communication by gestures for deaf and mute based...
Abstract: The fact that technology is advancing as per Moore’s law, the attention towards deaf and mute individuals with hi-tech technology is not much. Deaf and mute have to communicate through sign language even for pithy things. And also many people did not understand this language. Now-a-days gesture is becoming an increasingly popular means of interacting with computers. This paper sheds light of an proposed potential idea relying on latest technology named Wi-See which was developed in Washington, US. This technology actually uses our conventional Wi-Fi signals for home automation by gesture recognition. So, depending upon this hi-tech technology, my modified application idea is towards deaf and dumb, especially, one who cannot speak, but knows English language for communication. Since wireless signals do not require line-of-sight and can traverse through walls, proposed idea can be very useful to expressed views by speechless people without requiring instrumentation of the human body with sensing devices. The whole idea is based on Doppler shift in frequency of Wi-Fi signals. Instead of controlling home appliances as by Wi-See, this idea extends its view for speech or words through speakers installed. Each successive pattern of English alphabet generated by Doppler shift by gestures in air, can be recorded and matched with predefined pattern, which when processed, be outputed through speaker as combined letter word ,inspired by English digital dictionary having prediction and correction algorithm. Keywords: Wi-Fi, Wi-See, Doppler shift, Gestures, Communication
Human Computer Interface Glove for Sign Language Translation
A human computer interface glove was developed with the aim of translating sign language to text & speech. The glove utilizes five flex sensors and an inertial measurement unit to accurately capture hand gestures. All components were placed on the backside of the glove providing the user with full range of motion, and not restricting the user from performing other tasks while wearing the glove.
Wearable Computing and Human Computer Interfaces
These slides discuss how improvements in ICs, MEMS, cameras, and other electronic components are making wearable computing and new forms of human-computer interfaces economically feasible. Improvements in digital signal processing ICs and MEMS-based microphones are rapidly improving the technical and economical feasibility of voice-recognition based interfaces. Improvements in 2D and 3D image sensors (e.g., camera ICs) are rapidly improving the technical and economical feasibility of gesture-based interfaces, augmented reality, and virtual reality. Improvements in ICs, MEMS, displays and other components are rapidly making many forms of wearable computing economically feasible; these include many forms of head, arm, torso, and leg-mounted displays. Improvements in the materials for both non-invasive and invasive brain scans are rapidly improving the technical and economical feasibility of neural interfaces.
Similar to Project handtalk (20)
Survey Paper on Raspberry pi based Assistive Device for Communication between...
Survey Paper on Raspberry pi based Assistive Device for Communication between...
A Translation Device for the Vision Based Sign Language
A Translation Device for the Vision Based Sign Language
Glove based wearable devices for sign language-GloSign
Glove based wearable devices for sign language-GloSign
AlterEgo: A Personalized Wearable Silent Speech Interface
AlterEgo: A Personalized Wearable Silent Speech Interface
HCI BASED APPLICATION FOR PLAYING COMPUTER GAMES | J4RV4I1014
HCI BASED APPLICATION FOR PLAYING COMPUTER GAMES | J4RV4I1014
Electronic Hand Glove Through Gestures For Verbally Challenged Persons
Electronic Hand Glove Through Gestures For Verbally Challenged Persons
Review Paper on Two Way Communication System with Binary Code Medium for Peop...
Review Paper on Two Way Communication System with Binary Code Medium for Peop...
Procedia Computer Science 94 ( 2016 ) 295 – 301 Avail.docx
Procedia Computer Science 94 ( 2016 ) 295 – 301 Avail.docx
Wireless and uninstrumented communication by gestures for deaf and mute based...
Wireless and uninstrumented communication by gestures for deaf and mute based...
Human Computer Interface Glove for Sign Language Translation
Human Computer Interface Glove for Sign Language Translation
Project handtalk
- 1. HANDTALK PRESENTATION BY: GAURAV RAJESH (1017415) JOSEPH EMMANUEL (1017417) SHRUTHI S (1017430)
- 2. Abstract: It is well-known that deaf and mute people find it very challenging to interact with those who are different from them. HandTalk unleashes as a novel technology that can aid those differently-able (deaf and mute) who have difficulty in communicating with others. The existing gesture recognition gloves might solve the problem but they are very expensive. HandTalk comes into play at this point as a problem-solver; it is accurate and inexpensive.
- 3. Introduction: Of all the available Sign Languages, The American Sign Language (ASL) serves as the major form of communicative method for the deaf and mute in most communities. By incorporating the ASL in a portable device, the gestures made by the user can be recognized accurately by developing reliable software meant to serve its purpose. By ‘serve its purpose’, we mean that the software is capable of detecting the gestures made by the user and mapping those recognized gestures into appropriate alphabets/words and then ultimately converting those text into speech.
- 4. Requirements Hardware ◦ P5 virtual glove ◦ IR receiver ◦ Computer with minimum of 512 MB RAM and 100 MB Free HDD Space. Software ◦ Text to speech synthesizer ◦ To convert gestures to text
- 5. UI P5 Virtual Reality Glove 3D and virtual environments proprietary bend sensor
- 6. Motivation 250-300 million peoples. Indian situations. Worldwide situations.
- 7. The Design: We characterised four main modules that comprises the system. 1. The UI module deals with the P5 glove, its features and composition, and its interaction with the system. 2. The Communication module accounts for the mode of communication technology employed; which is Infrared that is bundled with the P5 glove. 3. The Processing module is the vital one where the major actions such as the finger bend data being received constantly and interpreted with the software, which looks into the gesture dictionary and once matched would pop out the appropriate text data. The textual data that is given out is further taken by a speech synthesizer which transforms into audio and made available to the users. 4. The Output module does only have a very minor role when compared with the other three; its main concern is the delivery of the output to the user.
- 9. CONVERSION DELAY TEXT PREVIEW AND LOWER GESTURE PREVIEW TIME SWITCH FREE SUPPORT FOR GESTURE DICTIONARY EDITABLE GESTURE DATABASE INSTANT SPEECH SPEAK ANYTIME
- 10. The User Interface: HandTalk uses component off-the-shelf (COTS) called P5 Virtual Reality Glove which is mainly used for 3D gaming. User-friendly interface that the deaf can use to interact with the software to get what they want. It is inexpensive, light-weight and portable which fits into every aspect of our scheme. The glove is built upon flex sensors, as its name indicates, the sensors are flexible and it works on the unique property of its resistance being varied depending upon the degree of the finger bends. The glove is embedded with eight infrared transmission points which constantly transfer data to the IR module which in turn is connected to the computer through a USB port.
- 12. Problems with existing system Meta Motion’s Data Glove Series and UC Berkeley’s Accelerating Glove Sensing prototype Expensive Only gesture recognition. Time delay problem. Buffer problem
- 14. AND MORE…! Future enhancements: Wired glove into Wireless glove. Bluetooth instead of Infrared. Mobile replaces laptop. Support for Natural Language Processing. Embedded Chip.
- 17. Literature survey Isaac Garcia Incertis, Jaime Gomez Garcia-Bermejo, Eduardo Zalama Casanova, "Hand Gesture Recognition for Deaf People Interfacing," icpr, vol. 2, pp.100-103, 18th International Conference on Pattern Recognition (ICPR'06) Volume 2, 2006. John Kangchun Perng, Brian Fisher, Seth Hollar, Kristofer S.J. Pister, "Acceleration Sensing Glove," iswc, pp.178, Third International Symposium on Wearable Computers (ISWC'99), 1999 Ali Farhadi, David Forsyth, Ryan White, "Transfer Learning in Sign language," cvpr, pp.1-8, 2007 IEEE Conference on Computer Vision and Pattern Recognition, 2007
- 18. Jiyong Ma, Wen Gao, Jiangqin Wu, Chunli Wang, "A Continuous Chinese Sign Language Recognition System," fg, pp.428, Fourth IEEE International Conference on Automatic Face and Gesture Recognition (FG'00), 2000 Sylvie C.W. Ong, Surendra Ranganath, "Automatic Sign Language Analysis: A Survey and the Future beyond Lexical Meaning," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 6, pp. 873-891, June 2005, doi:10.1109/TPAMI.2005.112 ”Experiments in Virtual Reality” by David Harrison and Mark Jaques. Year of Publication: 1996, Publisher: Butterworth-Heinemann (Newton, USA) ”The Virtual Reality Homebrewer’s Handbook” by Robin Hollands, Sean Clark and Chris Hand Year of Publication: 1996, Publisher: John Wiley & Sons, Inc. (New York, USA) http://www.cwonline.com ”The American Sign Language Handshape Dictionary” by Richard A. Tennant and Marianne Gluszak Brown. Year of Publication: 1998, Publisher: Gallaudet University Press
- 19. THANK YOU