A New Data Glove Approach for Malaysian Sign Language Detection

Shukor, Ahmad Zaki; Miskon, Muhammad Fahmi; Jamaluddin, Muhammad Herman; Ali@Ibrahim, Fariz bin; Asyraf, Mohd Fareed; Bahar, Mohd Bazli

doi:10.1016/j.procs.2015.12.276

Cited by 127 publications

(53 citation statements)

References 10 publications

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“…Currently, the most effective tools for capturing hand movement are electromechanical devices or magnetic sensors (data gloves) [8,9,10,11]. Despite the great accuracy of the data they have the disadvantages in terms of occasional use because they are very expensive, they hinder the natural movement of the hand and require complex calibration and configuration procedures to obtain accurate measurements.…”

Section: Reviewmentioning

confidence: 99%

Validation of a Digital Interface for Assessment of Motor Function Based on MFM

Souza

Vincent-Genod

Vuillerot

et al. 2018

Human Systems Engineering and Design

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Patients with neuromuscular diseases undergo frequent motor assessments. The MFM scale (Motor Function Measure) is a validated scale for all neuromuscular diseases. Tasks 18, 19, and 22 have the potential to have a digitized version on a Tablet. This article proposes to digitalize a first version of items 18, 19 and 22 based on MFM. The results show that the scores were similar for the Tablet version and the paper version.

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Section: Reviewmentioning

confidence: 99%

Validation of a Digital Interface for Assessment of Motor Function Based on MFM

Souza

Vincent-Genod

Vuillerot

et al. 2018

Human Systems Engineering and Design

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“…The sensor-based approach uses some sensors and devices in order to get the captured motion, position and velocity, from the literature we can summarize the used sensors as [20]: (a) (IMU) Inertial measurement unit which is used for recording the body position and velocity, (b) Electromyography (EMG) it is electrodiagnostic medicine device which is used for measuring and recording the human muscle's pulse in order to detect the body parts activity, (c) Radar and Wi-Fi, it uses the radio frequency signals in order to detect the human activities and body motion, (d) Other approaches depending on data gloves which are fabricated from flex sensors or including electromagnetics or ultrasonic techniques, the using of gloves is the common and popular used approach in sensor-based approach. For non-Arabic systems, Raut proposed a recognition system for 10 isolated signs using sensor-based approach, the gloves were designed from flex sensors and accelemeter [21]. Rajamohan uses data gloves which were fabricated from 5 flex sensors and one accelerometer added to some tactile sensors, they were used to recognize 13 static American alphabets, the accuracy reached 90% [22].…”

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.

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“…In the sensor-based approach the deaf needs to wear Electronic Gloves which contains a number of sensors during the performance of the gestures. In the visual-based approaches Images of the signer are captured by a camera and video processing is done to perform detection of the sign language [3].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Arabic Sign Language to Arabic text Translation for Easy Deaf Communication

Alfi¹,

Atawy²

2018

IJCA

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This paper presents an intelligent translation system for the signs of some words and letters in the Arabic sign language. The proposed translation system does not depend on any visual markings or gloves used to complete the recognition process. The proposed translation system deals with images, which allows the user to interact with the system in a natural way. The proposed translation system consists of four main phases; Preprocessing images phase, feature extraction phase, matching strategy phase, and Display Text Translation phase. The extracted features used are combining intensity histogram features and Gray Level Co-occurrence Matrix (GLCM) features, Experiments revealed that the proposed system was able to recognize the 19 Arabic alphabets and word with an accuracy of 73%.

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A New Data Glove Approach for Malaysian Sign Language Detection

Cited by 127 publications

References 10 publications

Validation of a Digital Interface for Assessment of Motor Function Based on MFM

Validation of a Digital Interface for Assessment of Motor Function Based on MFM

Supervised learning classifiers for Arabic gestures recognition using Kinect V2

Intelligent Arabic Sign Language to Arabic text Translation for Easy Deaf Communication

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