A simple sign language recognition system based on data glove

Wu, Jiangqin; Guo, Wen; Song, Yuanlin; Liu, Wei; bo, Pang

doi:10.1109/icosp.1998.770847

Cited by 26 publications

(8 citation statements)

References 4 publications

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“…The experiment had satisfactory results, which recognized 3 to 4 characters per second, although the researchers did not use any classifier. Meanwhile, Jiangqin et al published the paper about Chinese Sign Language Recognition system [6].…”

Section: Cyber Glovesmentioning

confidence: 99%

American Sign Language Recognition system by using surface EMG signal

Savur

Sahin

2016

2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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Sign Language Recognition (SLR) system is a novel method that allows hard of hearing people to communicate with society. In this study, an American Sign Language (ASL) recognition system was proposed by using the surface Electromyography (sEMG). The objective of this study is to recognize the American Sign Language alphabet letters and allow users to spell words and sentences. For this purpose, sEMG signals are acquired from subject's right forearm for 27 American Sign Language gestures, 26 English alphabet letters, and one for home position. Time domain, frequency domain (band power), power spectral density (band power), and average power features were used as the feature extraction methods. After feature extraction, Principal Component Analysis (PCA) was applied to obtain uncorrelated features. As a classification method, Support Vector Machine and Ensemble Learning algorithm were used and their performances were compared with tabulated results. In conclusion, the results of this study show that sEMG signal can be used for SLR systems. vi List of Contributions • Implementation of a new input method for users who know American Sign Language. • Design of a smart phone application that allows hard of hearing people to communicate with hearing people. • Comparison of results from the Electromyography signal collected for Sign Language Recognition System using four different feature extraction methods: Time domain features, frequency fomain band powers, power spectral density(PSD) band power, and average power in each channel in combination with two different classification metods: Support Vector Machine (SVM) and Ensemble Learning (Bagged Trees).

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Section: Cyber Glovesmentioning

confidence: 99%

American Sign Language Recognition system by using surface EMG signal

Savur

Sahin

2016

2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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“…From the literature survey, we have understood that the models proposed for sign language recognition address the problem either at finger spelling level [22,26,28,29] or at word level [3,10,25,32,34]. Since signs used by hearing impaired people are very abstract, the sign language recognition based on fingerspelling or word seems to be cumbersome and not effective.…”

Section: Related Workmentioning

confidence: 99%

Indian sign language recognition: An approach based on fuzzy-symbolic data

Nagendraswamy

Kumara²,

Chinmayi³

2016

2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI)

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In this paper, the task of recognizing signs made by hearing impaired people at sentence level has been addressed. A novel method of detecting sign boundaries in a video of continuous signs has been proposed and extraction of spatial features to capture hand movements of a signer through fuzzy membership functions has been proposed. Frames of a given video of a sign are preprocessed to extract face and hand components of a signer. The centroids of the extracted components are exploited to extract spatial features. The concept of interval valued type symbolic data has been explored to capture variations in the same sign made by the different signers at different instances of time. A suitable symbolic similarity measure is studied to establish matching between reference and test signs and a simple nearest neighbor classifier is used to recognize an unknown sign as one among the known signs by specifying a desired level of threshold. An extensive experimentation is conducted on a significantly large corpus of Indian regional signs created by us during the course of our research work in order to evaluate the performance of the proposed system.

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“…It interprets 26 signs in Chinese Sign Language using 18 sensors and a 3-D tracking system based on neural networks to recognize stance, orientation of the palm, its position and course of movement. This simulation-based motion system is sold and used in multiple applications (Jiangquin, Wen, Yibo, Wei &Bo, 1998 andSwee, Ariff, Sh-hussain &Seng, 2007).…”

Section: Recognition Systemsmentioning

confidence: 99%

Tools Facilitating Communication for the Deaf

et al. 2014

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There is a great deal of interest worldwide in serving people with diverse abilities and needs, especially in the fi eld of education. This implies a commitment to inclusion, empowerment and creating opportunities in a range of sectors. The deaf pose a considerable challenge to any proposal for inclusion and social integration, because their diffi culty aff ects communication. The purpose of this study is to contribute to theoretical refl ection on the development of communication tools that give the

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A simple sign language recognition system based on data glove

Cited by 26 publications

References 4 publications

American Sign Language Recognition system by using surface EMG signal

American Sign Language Recognition system by using surface EMG signal

Indian sign language recognition: An approach based on fuzzy-symbolic data

Tools Facilitating Communication for the Deaf

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