A new approach for designing a smart glove for Arabic Sign Language Recognition system based on the statistical analysis of the Sign Language

Sadek, Mina I.; Mikhael, Michael N.; Mansour, Hala A.

doi:10.1109/nrsc.2017.7893499

Cited by 39 publications

(22 citation statements)

References 10 publications

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“…Several of the obtained articles (7/71) fit the developed category concerning our taxonomy, because they did not develop a new system. Rather, they (3/7) served as an eligible framework of the template [ 48 , 49 , 52 ]. Other articles (3/7) addressed the system design [ 42 , 73 , 75 ], whereas one article presented the development and use of a method or technique for the SL system [ 72 ].…”

Section: The Analysis Resultsmentioning

confidence: 99%

“…Through the proposed architecture and algorithm, the recognition accuracy will be acceptable. In [ 49 ], the framework for a sensory glove for Arabic sign language signs (ArSL) is presented. The glove design is based on statistical analysis for all words of ArSL in terms of a single hand via as few sensors as possible.…”

Section: The Analysis Resultsmentioning

confidence: 99%

“…Combining a 3-axis ACC and a 3-axis gyroscope on the same board makes it suited for measuring tracked motion; the device presents data regarding accelerations in all three directions plus rotations around each axis [ 42 , 48 , 49 ]. Gyroscopes measure angular velocity and how fast something is spinning about an axis.…”

Section: Methodsmentioning

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

194

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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.

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Section: The Analysis Resultsmentioning

confidence: 99%

Section: The Analysis Resultsmentioning

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

194

View full text Add to dashboard Cite

show abstract

“…Data glove approaches use types of sensors to capture the position and motion for the hand. In these approaches can compute in easy and accurate the coordinates of the locations of the fingers palm, and hand configurations [10][11][12][13][14][15][16]. The sensors do not achieve an easy connection with the computer because it needs to be the user connected physically with the computer and hinder the movement of the hand.…”

Section: Introductionmentioning

confidence: 99%

A new hand gestures recognition system

Alsaedi

Alasadi

2020

IJEECS

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<p>Talking about gestures makes us return to the historical beginning of human communication, because, in fact, there is no language completely free of gestures. People cannot communicate without gestures. Any action or movement without gestures is free of real feelings and cannot express the thoughts. The purpose from any hand gestures recognition system is to recognizes the hand gesture and used it to transfer a certain meaning or for computer control or and device. Our paper introduced a low cost system to recognize the hand gesture in real-time. Generally, the system divided into five steps, one to image acquisition, second to pre-processing the image, third for detection and segmentation of hand region, four to features extraction and five to count the numbers of fingers and gestures recognition. The system has coded by Python language, PyAutoGUI library, OS Module of Python and the OpenCV library.</p>

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“…Mohandes [26] proposed ArSL recognition system using Cyberglove in order to track 100 two-handed signs with 20 samples and achieved accuracy is 99.6%. Sadek et al [27] proposed a hand gesture recognition using a smart glove which was designed from a set of sensors, the recognition was based on a statistical analysis of the hand shape of during performing the 1300 words of the Arabic sign language (ArSL). Microsoft Kinect is Almeida et al [12] 34 Brazilian pre-segmented words SVM classifier (support vector machine) 80 Elleuch and Wali [13] 5 American Pre-segmented words Multiclass SVM classifier (support vector machine) 96.8 Geng et al [14] 20 Chinese words 69.32 Halim et al [15] 20 Pakstanina pre-segmented words DTW (dynamic time wrapping) 87 Dong et al [16] 26 a motion sensing input device by Microsoft.…”

Section: Introductionmentioning

confidence: 99%

Supervised learning classifiers for Arabic gestures recognition using Kinect V2

Hisham

Hamouda

2019

SN Appl. Sci.

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Human-Computer Interaction (HCI) refers to the interaction between the computers and human. One of the most important applications of (HCI) is sign language recognition. Several research works aimed to interpret and translate the sign language to a spoken language to help the hear impaired persons in integrating with communities. Sign language is the main way of communication for the deaf persons and hearing impaired which enable them to communicate with their societies and between each other's. According to the World Health Organization, there are 466 million hearing loss people (i.e. 5% of the world population), 432 million or 93% of them are adults and 34 million or 17% of them are children. The hearing-impaired persons often need to the same level of mental capability as the normal persons. The main problem is the most of the hearing persons who cannot understand the sign language and most of hearing impaired cannot read or write our spoken language, this represents a barrier between the deaf persons and their societies so developing an automatic sign language recognition system is very necessary. This research introduces dynamic Arabic Sign Language recognition system using Microsoft Kinect. The recognition depends on using two machine learning algorithms (a) Decision Tree and (b) Bayesian Network then applied Ada-Boosting technique to enhance the recognition of the system, we compared the results with two direct matching techniques: (a) Dynamic Time Wrapping and Hidden Markov Model the system was applied on 42 Arabic gestures related to medical field. The experimental results showed that the proposed system recognition rate reached 91.18% for Decision Tree classifier, 92.50% for Bayesian classifier and 93.7% after applying Ada-Boosting.

show abstract

A new approach for designing a smart glove for Arabic Sign Language Recognition system based on the statistical analysis of the Sign Language

Cited by 39 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

A new hand gestures recognition system

Supervised learning classifiers for Arabic gestures recognition using Kinect V2

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