Glove-Based Continuous Arabic Sign Language Recognition in User-Dependent Mode

Tubaiz, Noor; Shanableh, Tamer; Assaleh, Khaled

doi:10.1109/thms.2015.2406692

Cited by 144 publications

(70 citation statements)

References 19 publications

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“…Here, six calibration steps were defined to adapt to different hand sizes. More recently, Tubaiz et al [20] worked on the DG5-VHand data glove system, which consists of 5 bend sensors and a 3-DOF accelerometer for both hands to recognize 40 sentences in the Arabic sign language. The data glove communicates with a computer via Bluetooth and a camera is used to collect the data.…”

Section: Related Workmentioning

confidence: 99%

Real-Time and Embedded Detection of Hand Gestures with an IMU-Based Glove

et al. 2018

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This article focuses on the use of data gloves for human-computer interaction concepts, where external sensors cannot always fully observe the user's hand. A good concept hereby allows to intuitively switch the interaction context on demand by using different hand gestures. The recognition of various, possibly complex hand gestures, however, introduces unintentional overhead to the system. Consequently, we present a data glove prototype comprising a glove-embedded gesture classifier utilizing data from Inertial Measurement Units (IMUs) in the fingertips. In an extensive set of experiments with 57 participants, our system was tested with 22 hand gestures, all taken from the French Sign Language (LSF) alphabet. Results show that our system is capable of detecting the LSF alphabet with a mean accuracy score of 92% and an F1 score of 91%, using complementary filter with a gyroscope-to-accelerometer ratio of 93%. Our approach has also been compared to the local fusion algorithm on an IMU motion sensor, showing faster settling times and less delays after gesture changes. Real-time performance of the recognition is shown to occur within 63 milliseconds, allowing fluent use of the gestures via Bluetooth-connected systems.

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Section: Related Workmentioning

confidence: 99%

Real-Time and Embedded Detection of Hand Gestures with an IMU-Based Glove

et al. 2018

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“…Tubaiz et al [19] have anticipated a glove-based Arabic sign language appreciation system using a method for progressive data arrangement. In the dataset, hand actions are seized using two DG5-VHand data gloves.…”

Section: Literature Reviewmentioning

confidence: 99%

Sign Gesture Recognition Using Modified Region Growing Algorithm and Adaptive Genetic Fuzzy Classifier

Kaluri

Reddy²

2016

IJIES

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Sign language is the most communal language used by the deaf people among themselves for any kind of communication. People who are not familiar with Sign Language face difficulty in interacting with the deaf people.Hence, an effective system should be established to attain and discriminate the sign language. In our proposed system, we developed a framework for extracting and recognizing the sign gesture language and which is classified into four stages like noise removal using median filter, Segmentation using Modified Region Growing Algorithm (MRGA), feature extraction and recognition using Adaptive Genetic Fuzzy Classifier (AGFC). We have used Genetic algorithm with Fuzzy classifier to find out the optimal rules generated by Fuzzy classifier.

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“…In directing this examination, it is important to decipher a few elements of the video, for example, discourse, signal and its graphic components, stance and outward appearance, playing out an undertaking otherwise called video comment. One phase of signal interpretation is the motion stage division, which is normally performed physically by experts and is a relentless assignment [16]. Since various people have distinctive hands and fingers in physical measurement, a similar type of a motion made by two distinct people may create nonidentical numerical information when the motions are measured by normal detecting gadgets [17,18].…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Framework for Sign Gesture Recognition using Hidden Markov Model and Adaptive Histogram Technique

Kaluri¹,

Reddy²

2017

IJIES

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Gesture based communication is the fundamental method for correspondence for those with hearing and vocal incapacities. Communication via gestures comprises of making shapes or developments with human hands as for the head or other body parts. In this paper, we propose a new framework for recognizing sign gestures by using Hidden Markov Model (HMM) and Histogram based methods. Initially, the noise of an image will be eliminated by Wiener Filter and the image will be segmented with the help of Histogram oriented methods -Adaptive Histogram technique and then features will be extracted. The extracted features will be given to the HMM for training and recognition of gestures. Our experimental results show a better performance in terms of recognizing gestures from a blurred image compared to the existing segmentation methods.

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Glove-Based Continuous Arabic Sign Language Recognition in User-Dependent Mode

Cited by 144 publications

References 19 publications

Real-Time and Embedded Detection of Hand Gestures with an IMU-Based Glove

Real-Time and Embedded Detection of Hand Gestures with an IMU-Based Glove

Sign Gesture Recognition Using Modified Region Growing Algorithm and Adaptive Genetic Fuzzy Classifier

An Enhanced Framework for Sign Gesture Recognition using Hidden Markov Model and Adaptive Histogram Technique

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