Sign language recognition for hearing impaired people based on hands symbols classification

Kumar, Naresh

doi:10.1109/ccaa.2017.8229808

Cited by 12 publications

(14 citation statements)

References 13 publications

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“…After adding additional 7 records identified by hand and grey literature searching, a total of 24 publications were judged to meet the inclusion criteria and their data were extracted (Figure 1). 18 reported from 11 different countries: Bulgaria, 22 Egypt, 23,24 India, 11,12,[25][26][27][28][29][30][31][32][33] Indonesia, [34][35][36] Iran, 37 Malaysia, 38 Mexico, 39 Pakistan, 40 Saudi Arabia, 41 Thailand 42 and the United Arab Emirates (UAE). 43 Most of the studies were conducted in the last decades and included three types of study designs including randomized trials, 11,12,23,[25][26][27]37,[40][41][42][43] non-randomized trials, 28,29,33,34,36 and pre-post intervention without control studies.…”

Section: Resultsmentioning

confidence: 99%

“…18 reported from 11 different countries: Bulgaria, 22 Egypt, 23,24 India, 11,12,[25][26][27][28][29][30][31][32][33] Indonesia, [34][35][36] Iran, 37 Malaysia, 38 Mexico, 39 Pakistan, 40 Saudi Arabia, 41 Thailand 42 and the United Arab Emirates (UAE). 43 Most of the studies were conducted in the last decades and included three types of study designs including randomized trials, 11,12,23,[25][26][27]37,[40][41][42][43] non-randomized trials, 28,29,33,34,36 and pre-post intervention without control studies. 22,24,[30][31][32]35,38,39 Randomized controlled trials were further divided into parallel trials, 11,…”

Section: Resultsmentioning

confidence: 99%

“…These were educational videos 27,38,40,42 with sign language 11,32,37,41 or with subtitles, 26 movies with sign language, 43 cartoon videos with sign language, 35 presentations with images and sign language, 39 skit videos, 29 booklets, 38,41 illustrated book, 42 storybooks, 34 2D and 3D books, 36 pictures, 40 posters, 25 charts, 32 photographic instructions, 11 flip chart, 32 models 32 with sign language explanation, 37 leaflets, 32 multi-media virtual patient learning module using an overhead projector, 28 cartoons, 22 slide films, 22 plastic dolls, 22 models and porcelain figurines of 'dental devils'. 22 The sign language interpreter 33 and sign language interpretation application 33 formed part of some of the studies.…”

Section: Classification Of Visual Learning Strategiesmentioning

confidence: 99%

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Visual learning strategies for oral health promotion and treatment interventions in children and adolescents with hearing impairment – A systematic review

Shrivastava

Gupta

Nishant

et al. 2022

Special Care in Dentistry

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AimPeople with hearing impairment (HI) prefer visual learning strategies in daily activities owing to their reliance on vision. The aim of this systematic review is to evaluate the effectiveness of visual learning as a communication strategy in improving oral hygiene and dental care of children and adolescents with HI.Methods and ResultsFour electronic databases were searched and complemented by hand searching for original intervention studies published till December 2021. Eligible studies were screened, data was extracted as per priori data collection form and analyzed by thematic content analysis. The quality of studies was assessed as per the validated tools appropriate for study designs including ROB2, ROBINS‐I, and NIH quality assessment tool. Out of 4159 records identified and 1302 duplicates removed, 24 original studies were identified and the visual learning strategies were classified into sign language, visual aids with or without sign language, customized educational demonstrations and the interim role of training the teachers. These strategies helped in improving oral hygiene status, oral health‐related knowledge and attitude as well as dental anxiety during treatment. Participants were found to be satisfied with these strategies, however, significant heterogeneity in the included studies precluded meaningful meta‐analysis. ROB2 and ROBINS were rated as high and serious in all included trials, respectively, and NIH Quality Assessment Tool for Pre‐Post Studies With No Control as fair in five studies and poor in three.ConclusionOur findings emphasize the significance of visual learning, however, long‐term rigorously designed trials are needed to better understand effective and patient‐centered communication methods for people with HI.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Classification Of Visual Learning Strategiesmentioning

confidence: 99%

See 1 more Smart Citation

Visual learning strategies for oral health promotion and treatment interventions in children and adolescents with hearing impairment – A systematic review

Shrivastava

Gupta

Nishant

et al. 2022

Special Care in Dentistry

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“…The feature extraction aid in accuracy improvement, and speed [47]. Some of these feature extraction method include SURF (Speeded Up Robust Feature) [34], speed up robust feature (Laplace of Gaussian with box filter) [34], SIFT (shift-invariant feature transform) [33], PCA (Principal Component Analysis) [37], [4], LDA (Linear Discriminant Analysis) [48], Convexity defects and k-curvature [49], time domain to frequency domain [31], [35], Local binary pattern, etc. The feature extraction methods used for SLR-based study is tabulated in Table III.…”

Section: B Feature Extractionmentioning

confidence: 99%

A Comprehensive Review of Sign Language Recognition: Different Types, Modalities, and Datasets

Madhiarasan¹,

Roy²

2022

Preprint

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A machine can understand human activities, and the meaning of signs can help overcome the communication barriers between the inaudible and ordinary people. Sign Language Recognition (SLR) is a fascinating research area and a crucial task concerning computer vision and pattern recognition. Recently, SLR usage has increased in many applications, but the environment, background image resolution, modalities, and datasets affect the performance a lot. Many researchers have been striving to carry out generic real-time SLR models. This review paper facilitates a comprehensive overview of SLR and discusses the needs, challenges, and problems associated with SLR. We study related works about manual and non-manual, various modalities, and datasets. Research progress and existing stateof-the-art SLR models over the past decade have been reviewed. Finally, we find the research gap and limitations in this domain and suggest future directions. This review paper will be helpful for readers and researchers to get complete guidance about SLR and the progressive design of the state-of-the-art SLR model.

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“…All the features of processed frames are then extracted using Fourier descriptor method. Instead of using pre-processing techniques like filtering and segmentation of hand gesture, methods such as scaling and shifting parameters were extracted based on high low frequency of images up-to 7th level [47]. Fusion of morphological process and canny edge operator with DWT is done to detect boundary pixels of the hand image [48].…”

Section: Cbirmentioning

confidence: 99%

Feature Extraction Technique for Vision-Based Indian Sign Language Recognition System: A Review

Tyagi

Bansal

2020

Computational Methods and Data Engineering

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Vision-based sign language recognition system is an emerging research area in human-computer interaction (HCI). It has been proved as a powerful communication tool for deaf and mute society, irrespective of geographical differences. Automatic sign language recognition system works broadly in three phases namely image pre-processing, feature extraction and classification of gestures. The output of the feature extraction phase is crucial for various classifiers. This paper presents a comprehensive review of feature extraction techniques used in vision-based sign language recognition system. A taxonomy of currently used techniques for feature extraction has been presented. The paper concludes by presenting future direction in feature extraction technique for Indian sign language (ISL).

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Sign language recognition for hearing impaired people based on hands symbols classification

Cited by 12 publications

References 13 publications

Visual learning strategies for oral health promotion and treatment interventions in children and adolescents with hearing impairment – A systematic review

Visual learning strategies for oral health promotion and treatment interventions in children and adolescents with hearing impairment – A systematic review

A Comprehensive Review of Sign Language Recognition: Different Types, Modalities, and Datasets

Feature Extraction Technique for Vision-Based Indian Sign Language Recognition System: A Review

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