Techno-talk: An American Sign Language (ASL) Translator

Arif, Arslan; Rizvi, Syed Tahir Hussain; Jawaid, Iqra; Waleed, Muhammad Adam; Shakeel, Muhammad Raheel

doi:10.1109/codit.2016.7593642

Cited by 21 publications

(16 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The microcontroller is the system’s mind that is accountable for gathering the data from the sensors provided by the glove and performing the required processing of these data to recognize and transfer the sign to the output port to be presented in the final stage. A high-performance microcontroller with a microchip with 8-bit AVR microcontrollers based on reduced instruction set computer (RISC), which combines 32KB in-system programming (ISP) flash memory with read–write capabilities called ATmega ( Figure 13 a), was used in [ 8 , 9 , 35 , 50 ]. A micro-controller MSP430G2553 (TEXAS INSTRUMENTS, Dallas, TX, USA), ( Figure 13 b) with an 8-channel, 10-bit (analogue to digital converter) ADC was used [ 37 ].…”

Section: Methodsmentioning

confidence: 99%

“…Several Arduino boards are available on the market such as Arduino Nano, Uno, Mega, etc. ; for instance, Arduino Uno (Arduino, Italy), ( Figure 13 c) is based on the ATmega328P microcontroller and has 14 digital inputs/outputs, 6 analog inputs, a 16 MHz quartz crystal, and USB connection [ 6 , 8 , 21 , 37 , 56 , 57 , 58 , 59 ]. Odroid XU4 (Hardkernel2, Anyang, South Korea) ( Figure 13 d), produced by Hardkernel2, uses a Samsung Exynos5 Octa 5410 processor that consists of 4 CPU cores each with Cortex™-A15 cores; it was used for the development of the data glove [ 59 ].…”

Section: Methodsmentioning

confidence: 99%

“…This can be achieved by developing a glove equipped with sensors and an electronic circuit. Several benefits of using this device are that no complex data processing is needed [ 4 ]; there are no limitations on movements such as sitting behind a desk or chair; hand shape recognition is not affected by the background condition [ 5 , 6 ]; it is a lightweight, SLR-based glove device that can be carried to make mobility easy and comfortable [ 7 , 8 ]; and it is a recognition system that can be employed for learning SL for both dumb and able people [ 9 ]. Furthermore, numerous kinds of applications are currently involved in gesture recognition systems such as SLR, substitutional computer interfaces, socially assistive robotics, immersive gaming, virtual objects, remote control, medicine-health care, gesture recognition of hand/body language, etc.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Review on Systems-Based Sensory Gloves for Sign Language Recognition State of the Art between 2007 and 2017

Ahmed

Zaidan

et al. 2018

Sensors

186

View full text Add to dashboard Cite

Loss of the ability to speak or hear exerts psychological and social impacts on the affected persons due to the lack of proper communication. Multiple and systematic scholarly interventions that vary according to context have been implemented to overcome disability-related difficulties. Sign language recognition (SLR) systems based on sensory gloves are significant innovations that aim to procure data on the shape or movement of the human hand. Innovative technology for this matter is mainly restricted and dispersed. The available trends and gaps should be explored in this research approach to provide valuable insights into technological environments. Thus, a review is conducted to create a coherent taxonomy to describe the latest research divided into four main categories: development, framework, other hand gesture recognition, and reviews and surveys. Then, we conduct analyses of the glove systems for SLR device characteristics, develop a roadmap for technology evolution, discuss its limitations, and provide valuable insights into technological environments. This will help researchers to understand the current options and gaps in this area, thus contributing to this line of research.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Review on Systems-Based Sensory Gloves for Sign Language Recognition State of the Art between 2007 and 2017

Ahmed

Zaidan

et al. 2018

Sensors

186

View full text Add to dashboard Cite

show abstract

“…The feature was extracted from the image as hand contour then the corresponding alphabet or meaning was described. [32][33][34] The data gloves sensor technique is more advantages than the image-based one and became a promising tool for communication [17,35,36]. These hand gestures can play a significant role in different fields as Robotics and Automatic control [22,37].…”

Section: Introductionsmentioning

confidence: 99%

Rational Design of a Novel Smart Mobile Communication System for Arabian Deaf and Dumb

Eldesoky¹,

Eid²,

Abdullah³

2019

Preprint

View full text Add to dashboard Cite

The deaf impairment is among the most substantial health problem worldwide, that can lead to various personal, economical, and social crisis. Therefore, it is critical for developing an efficient way to facilitate communication between deaf-dumb impaired and normal people. Herein, we have rationally designed a new digitally computerized and mobile smart system as an efficient communication tool between deaf impaired and normal Arabian people. This is based on two main steps, including creating a digital output for the hand gestures using gloves flex sensors equipped with a three-axis accelerometer that is controlled using a microcontroller. The digital results are compared to that in a words-based “database”, where Arabs use expressions not alphabet in their communication. The second step is translation or conversion the outputs of the first stage into written texts and voices. The newly developed system allows Arabian deaf to translate words of ordinary people into gestures using a speech recognition system with an impressive accuracy over 90 % without the needing for a webcam, colored gloves, and/or online translator. The presented system can be used on any android or windows.

show abstract

“…The synthesis phase of HTS system was used for the construction of HMM sequence by concatenating contextdependent HMMs. Similarly, Arif et al used five flex sensors on a glove to translate ASL gestures for Deaf-mute into the visual and audio output on LCD [29].…”

Section: Sensor-based Technologymentioning

confidence: 99%

A Novel Technique for Speech Recognition and Visualization Based Mobile Application to Support Two‐Way Communication between Deaf‐Mute and Normal Peoples

Yousaf

Mehmood

Saba

et al. 2018

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

Mobile technology is very fast growing and incredible, yet there are not much technology development and improvement for Deafmute peoples. Existing mobile applications use sign language as the only option for communication with them. Before our article, no such application (app) that uses the disrupted speech of Deaf-mutes for the purpose of social connectivity exists in the mobile market. The proposed application, named as vocalizer to mute (V2M), uses automatic speech recognition (ASR) methodology to recognize the speech of Deaf-mute and convert it into a recognizable form of speech for a normal person. In this work mel frequency cepstral coefficients (MFCC) based features are extracted for each training and testing sample of Deaf-mute speech. The hidden Markov model toolkit (HTK) is used for the process of speech recognition. The application is also integrated with a 3D avatar for providing visualization support. The avatar is responsible for performing the sign language on behalf of a person with no awareness of Deaf-mute culture. The prototype application was piloted in social welfare institute for Deaf-mute children. Participants were 15 children aged between 7 and 13 years. The experimental results show the accuracy of the proposed application as 97.9%. The quantitative and qualitative analysis of results also revealed that face-to-face socialization of Deaf-mute is improved by the intervention of mobile technology. The participants also suggested that the proposed mobile application can act as a voice for them and they can socialize with friends and family by using this app.

show abstract

Techno-talk: An American Sign Language (ASL) Translator

Cited by 21 publications

References 10 publications

A Review on Systems-Based Sensory Gloves for Sign Language Recognition State of the Art between 2007 and 2017

A Review on Systems-Based Sensory Gloves for Sign Language Recognition State of the Art between 2007 and 2017

Rational Design of a Novel Smart Mobile Communication System for Arabian Deaf and Dumb

A Novel Technique for Speech Recognition and Visualization Based Mobile Application to Support Two‐Way Communication between Deaf‐Mute and Normal Peoples

Contact Info

Product

Resources

About