Multi-Sign Language Glove based Hand Talking System

Baktash, Abdullah Qassim; Mohammed, Saleem Latteef; Jameel, Huda Farooq

doi:10.1088/1757-899x/1105/1/012078

Cited by 7 publications

(5 citation statements)

References 4 publications

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“…Vishal et al [5] Abdullah et al [10] developed the Hand Talking System (HTS), which uses a sewing controller and a few sensors. A smartphone, an accelerometer, an Arduino and flex sensors are included in the HTS.…”

Section: Literature Surveymentioning

confidence: 99%

A Review: Sign Language Converter Using Hand Gloves

Veeraprathap¹,

Shashank²,

Shetty³

et al. 2023

IJSREM

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Most people in the world are not able to communicate with other people because of different levels of disabilities caused by work accidents or some diseases. They are facing a lot of problems while communicating. They use sign language to communicate with normal people. But there is a communication barrier between normal and mute people while communicating. Therefore, the paper proposes a smart glove to convert sign language into voice. This system consists of a flex sensor, accelerometer and touch sensor attached to the gloves. These are used to sense the different gestures made by the mute people and couple the analogy voltage to the Arduino. After that, it is converted into speech using the speaker. So, this system helps mute people to communicate with normal people. Key Words: Flex sensor, Bluetooth, Accelerometer

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Section: Literature Surveymentioning

confidence: 99%

A Review: Sign Language Converter Using Hand Gloves

Veeraprathap¹,

Shashank²,

Shetty³

et al. 2023

IJSREM

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“…62 The use of RFID Tag could be used in management system for washing machine, which supports decisionmaking based on data provided by reading of RFID tags integrated with clothes, and the laundry device was integrated with cloud computing for garment management based on the unique identifier of the tags. [63][64][65] The radiation protective materials and smart textiles…”

Section: Computer Technology For Textiles and Apparelmentioning

confidence: 99%

Smart E-textiles: A review of their aspects and applications

Younes

2023

Journal of Industrial Textiles

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The innovation in high-converging technology using electronics and textiles augments the functionality of E-textiles as Smart E-Textiles (SETs), which overcome the gap between interactivity and inter-connectivity. The current article presents a review about the recent developments in Smart E-textiles and their relationship with other modern technologies. Smart E-Textiles can be developed via smart sensors, wireless communication technologies, embedded technologies, and nanotechnology, which can monitor activity for modern applications. Investments in research and development of Smart E-Textiles will encourage the use of them as sustainable and cost-effective options for light-weight, high-performance wearable technology for monitoring a wide range of the digitization activities of Smart E-Textiles applications.

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“…This is a haptic-based strategy in which flex sensors are used to capture physical values for processing [1]. Mohammed et al (2021) [3] developed a model that employs a glove equipped with sensors that can decode the American Sign Language (ASL) alphabet. On a single chip, the glove has flex sensors, touch sensors, and a 6 DOF accelerometer/gyroscope.…”

Section: Utilizing Cmos Cameramentioning

confidence: 99%

A New Intelligent Approach for Deaf/Dumb People based on Deep Learning

Elwahsh¹,

Elkhouly²,

Nasr³

et al. 2022

Computers, Materials &Amp; Continua

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People who are deaf or have difficulty speaking use sign language, which consists of hand gestures with particular motions that symbolize the "language" they are communicating. A gesture in a sign language is a particular movement of the hands with a specific shape from the fingers and whole hand. In this paper, we present an Intelligent for Deaf/Dumb People approach in real time based on Deep Learning using Gloves (IDLG). The approach IDLG offers scientific contributions based deep-learning, a multimode command techniques, real-time, and effective use, and high accuracy rates. For this purpose, smart gloves working in real time were designed. The data obtained from the gloves was processed using deep-learning-based approaches and classified multi-mode commands that allow dumb people to speak with regular people via their smart phone. Internally, the glove has five flex sensors and an accelerometer using to achieve Low-Cost Control System. The flex sensor generates a proportional change in resistance for each individual move. The processing of these hand gestures is in Atmega32A Microcontroller which is an advance version of the microcontroller and the lab view software. IDLG compares the input signal to memory-stored specified voltage values. The performance of the IDLG approach was verified on a dataset created using different hand gestures from 20 different people. In the test using the IDLG approach on 10,000 data points, process time performance of milliseconds was achieved with 97% accuracy.

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Multi-Sign Language Glove based Hand Talking System

Cited by 7 publications

References 4 publications

A Review: Sign Language Converter Using Hand Gloves

A Review: Sign Language Converter Using Hand Gloves

Smart E-textiles: A review of their aspects and applications

A New Intelligent Approach for Deaf/Dumb People based on Deep Learning

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