A pattern recognition model for static gestures in Malaysian sign language based on machine learning techniques

Ali H. Alrubayi; M. A. Ahmed; A. A. Zaidan; A. S. Albahri; B. B. Zaidan; O. S. Albahri; A. H. Alamoodi; Mamoun Alazab

doi:10.1016/j.compeleceng.2021.107383

A pattern recognition model for static gestures in Malaysian sign language based on machine learning techniques

Ali H. Alrubayi, M. A. Ahmed, A. A. Zaidan, A. S. Albahri, B. B. Zaidan, O. S. Albahri, A. H. Alamoodi, Mamoun Alazab

Research output: Contribution to journal › Article › peer-review

33 Citations (Scopus)

Abstract

This work proposes a pattern recognition model for static gestures in Malaysian Sign Language (MSL) based on Machine Learning (ML) techniques. The proposed model is divided into two phases, namely, data acquisition and data processing. The first phase involves capturing the required sign data, such as the shape and orientation of the hand, to construct a sensor-based SL dataset. The dataset is collected using a DataGlove device. This device is used to measure the motions of the fingers and wrists. Sixty-four features represent each sign in the dataset. The collected sensory dataset is cleaned in the second phase by removing redundant data. Then, the features are scaled and normalised to exhibit symmetrical behaviour and eliminate outliers. Then, ten different ML techniques are utilised based on real-time data for SL gesture recognition. Experimental results confirmed the efficacy of the proposed pattern recognition model compared with previous work.

Original language	English
Article number	107383
Pages (from-to)	1-15
Number of pages	15
Journal	Computers and Electrical Engineering
Volume	95
Early online date	17 Aug 2021
DOIs	https://doi.org/10.1016/j.compeleceng.2021.107383
Publication status	Published - Oct 2021

Bibliographical note

Funding Information:
The authors would like to thank the support of NVIDIA Corporation for the donation of the Quadro GPU used for this research.

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10.1016/j.compeleceng.2021.107383

Cite this

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title = "A pattern recognition model for static gestures in Malaysian sign language based on machine learning techniques",

abstract = "This work proposes a pattern recognition model for static gestures in Malaysian Sign Language (MSL) based on Machine Learning (ML) techniques. The proposed model is divided into two phases, namely, data acquisition and data processing. The first phase involves capturing the required sign data, such as the shape and orientation of the hand, to construct a sensor-based SL dataset. The dataset is collected using a DataGlove device. This device is used to measure the motions of the fingers and wrists. Sixty-four features represent each sign in the dataset. The collected sensory dataset is cleaned in the second phase by removing redundant data. Then, the features are scaled and normalised to exhibit symmetrical behaviour and eliminate outliers. Then, ten different ML techniques are utilised based on real-time data for SL gesture recognition. Experimental results confirmed the efficacy of the proposed pattern recognition model compared with previous work.",

keywords = "Hand gesture recognition, Human-machine interface, Machine learning techniques, Sensory glove, Sign language",

author = "Alrubayi, {Ali H.} and Ahmed, {M. A.} and Zaidan, {A. A.} and Albahri, {A. S.} and Zaidan, {B. B.} and Albahri, {O. S.} and Alamoodi, {A. H.} and Mamoun Alazab",

note = "Funding Information: The authors would like to thank the support of NVIDIA Corporation for the donation of the Quadro GPU used for this research. ",

year = "2021",

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doi = "10.1016/j.compeleceng.2021.107383",

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AU - Alrubayi, Ali H.

AU - Ahmed, M. A.

AU - Zaidan, A. A.

AU - Albahri, A. S.

AU - Zaidan, B. B.

AU - Albahri, O. S.

AU - Alamoodi, A. H.

AU - Alazab, Mamoun

N1 - Funding Information: The authors would like to thank the support of NVIDIA Corporation for the donation of the Quadro GPU used for this research.

PY - 2021/10

Y1 - 2021/10

N2 - This work proposes a pattern recognition model for static gestures in Malaysian Sign Language (MSL) based on Machine Learning (ML) techniques. The proposed model is divided into two phases, namely, data acquisition and data processing. The first phase involves capturing the required sign data, such as the shape and orientation of the hand, to construct a sensor-based SL dataset. The dataset is collected using a DataGlove device. This device is used to measure the motions of the fingers and wrists. Sixty-four features represent each sign in the dataset. The collected sensory dataset is cleaned in the second phase by removing redundant data. Then, the features are scaled and normalised to exhibit symmetrical behaviour and eliminate outliers. Then, ten different ML techniques are utilised based on real-time data for SL gesture recognition. Experimental results confirmed the efficacy of the proposed pattern recognition model compared with previous work.

AB - This work proposes a pattern recognition model for static gestures in Malaysian Sign Language (MSL) based on Machine Learning (ML) techniques. The proposed model is divided into two phases, namely, data acquisition and data processing. The first phase involves capturing the required sign data, such as the shape and orientation of the hand, to construct a sensor-based SL dataset. The dataset is collected using a DataGlove device. This device is used to measure the motions of the fingers and wrists. Sixty-four features represent each sign in the dataset. The collected sensory dataset is cleaned in the second phase by removing redundant data. Then, the features are scaled and normalised to exhibit symmetrical behaviour and eliminate outliers. Then, ten different ML techniques are utilised based on real-time data for SL gesture recognition. Experimental results confirmed the efficacy of the proposed pattern recognition model compared with previous work.

KW - Hand gesture recognition

KW - Human-machine interface

KW - Machine learning techniques

KW - Sensory glove

KW - Sign language

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JO - Computers and Electrical Engineering

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M1 - 107383

ER -

A pattern recognition model for static gestures in Malaysian sign language based on machine learning techniques

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