Two-way Smart Communication System for Deaf &amp; Dumb and Normal People

Gul, Areesha; Batool, Zehra; Shah, Sadia; Javed, Nazish; Saleem, Muhammad Imran

doi:10.1109/icisct49550.2020.9080028

Cited by 9 publications

(4 citation statements)

References 6 publications

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“…The system determines the sensor values and uses the threshold to determine which gesture it is. To validate the gesture probabilistic model is used, a very inefficient method was used in [35], [45] and [46], where the gestures were recorded manually. The user has to make the gesture and select the alphabet.…”

Section: Resultsmentioning

confidence: 99%

A review of factors that impact the design of a glove based wearable devices

Al-Khatib

Sheikh

2023

IJ-AI

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Loss of the capability to talk or hear applies psychological and social effects on the affected individuals due to the absence of appropriate interaction. Sign Language is used by such individuals to assist them in communicating with each other. The paper aims to report details of various aspects of wearable healthcare technologies designed in recent years based on the aim of the study, the types of technologies being used, accuracy of the system designed, data collection and storage methods, technology used to accomplish the task, limitations and future research suggested for the study. The aim of the study is to compare the differences between the papers. There is also comparison of technology used to determine which wearable device is better, which is also done with the help of accuracy. The limitations and future research help in determining how the wearable devices can be improved. A systematic review was performed based on a search of the literature. A total of 23 articles were retrieved. The articles are study and design of various wearable devices, mainly the glove-based device, to help you learn the sign language.

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Section: Resultsmentioning

confidence: 99%

A review of factors that impact the design of a glove based wearable devices

Al-Khatib

Sheikh

2023

IJ-AI

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“…Figure 5 representing that which dataset is the most used and which is the least used and as per the resulting signal based EMG data has only used twice in Khan et al (2020b) and Khan et al (2020a) whereas dataset which contains the images of sign used four times in Halim & Abbas (2014) , Kanwal et al (2014) , Nasir et al (2014) and Imran et al (2021a) . The most used type of dataset are the character-based datasets in Chandio et al (2020) , Naseem et al (2019) , Sagheer et al (2010) , Ahmad et al (2017) , Sami, (2014) , Husnain et al (2019) , Gul et al (2020) Arafat & Iqbal (2020) and Ahmed et al (2017) . Also, in Fig.…”

Section: Resultsmentioning

confidence: 99%

“…The study is either published in a journal or conference. From Table 3 , we can analyzed that the SVM ( Chandio et al, 2020 ; Imran et al, 2021a ; Sagheer et al, 2010 ; Ahmad et al, 2017 ; Khan et al, 2020b ; Ahmed et al, 2017 ; Imran et al, 2021b ) and Neural Network ( Chandio et al, 2020 ; Naseem et al, 2019 ; Ahmad et al, 2007 ; Arafat & Iqbal, 2020 ; Sagheer et al, 2009 ; Naz et al, 2015 ; Ul-Hasan et al, 2013 ) is the commonly used classifier by researchers for the detection of Urdu Sign Language other than these both rest of the classifiers used only once i.e., DTW ( Halim & Abbas, 2014 ), HMM ( Gul et al, 2020 ).…”

Section: Resultsmentioning

confidence: 99%

Recognition of Urdu sign language: a systematic review of the machine learning classification

Zahid

Rashid

Hussain³

et al. 2022

PeerJ Computer Science

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Background and Objective Humans communicate with one another using language systems such as written words or body language (movements), hand motions, head gestures, facial expressions, lip motion, and many more. Comprehending sign language is just as crucial as learning a natural language. Sign language is the primary mode of communication for those who have a deaf or mute impairment or are disabled. Without a translator, people with auditory difficulties have difficulty speaking with other individuals. Studies in automatic recognition of sign language identification utilizing machine learning techniques have recently shown exceptional success and made significant progress. The primary objective of this research is to conduct a literature review on all the work completed on the recognition of Urdu Sign Language through machine learning classifiers to date. Materials and methods All the studies have been extracted from databases, i.e., PubMed, IEEE, Science Direct, and Google Scholar, using a structured set of keywords. Each study has gone through proper screening criteria, i.e., exclusion and inclusion criteria. PRISMA guidelines have been followed and implemented adequately throughout this literature review. Results This literature review comprised 20 research articles that fulfilled the eligibility requirements. Only those articles were chosen for additional full-text screening that follows eligibility requirements for peer-reviewed and research articles and studies issued in credible journals and conference proceedings until July 2021. After other screenings, only studies based on Urdu Sign language were included. The results of this screening are divided into two parts; (1) a summary of all the datasets available on Urdu Sign Language. (2) a summary of all the machine learning techniques for recognizing Urdu Sign Language. Conclusion Our research found that there is only one publicly-available USL sign-based dataset with pictures versus many character-, number-, or sentence-based publicly available datasets. It was also concluded that besides SVM and Neural Network, no unique classifier is used more than once. Additionally, no researcher opted for an unsupervised machine learning classifier for detection. To the best of our knowledge, this is the first literature review conducted on machine learning approaches applied to Urdu sign language.

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“…The definition applauds a modern form of communicating by integrating the identification of hand motion with speech transition. They developed an android app for speech/text conversion with the aid of the Google API and have many required functionality such as emergency calls and position monitoring for treatment purposes ( Gul et al, 2020 ). Nasir et al (2014) propose a voice interpreter artificial sign language that starts by recording the 3D video stream by Kinect, and then focuses on the joints of concern in the human skeleton.…”

Section: Related Workmentioning

confidence: 99%

A computer vision-based system for recognition and classification of Urdu sign language dataset

Zahid

Rashid

Syed

et al. 2022

PeerJ Computer Science

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Human beings rely heavily on social communication as one of the major aspects of communication. Language is the most effective means of verbal and nonverbal communication and association. To bridge the communication gap between deaf people communities, and non-deaf people, sign language is widely used. According to the World Federation of the Deaf, there are about 70 million deaf people present around the globe and about 300 sign languages being used. Hence, the structural form of the hand gestures involving visual motions and signs is used as a communication system to help the deaf and speech-impaired community for daily interaction. The aim is to collect a dataset of Urdu sign language (USL) and test it through a machine learning classifier. The overview of the proposed system is divided into four main stages i.e., data collection, data acquisition, training model ad testing model. The USL dataset which is comprised of 1,560 images was created by photographing various hand positions using a camera. This work provides a strategy for automated identification of USL numbers based on a bag-of-words (BoW) paradigm. For classification purposes, support vector machine (SVM), Random Forest, and K-nearest neighbor (K-NN) are used with the BoW histogram bin frequencies as characteristics. The proposed technique outperforms others in number classification, attaining the accuracies of 88%, 90%, and 84% for the random forest, SVM, and K-NN respectively.

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Two-way Smart Communication System for Deaf & Dumb and Normal People

Cited by 9 publications

References 6 publications

A review of factors that impact the design of a glove based wearable devices

A review of factors that impact the design of a glove based wearable devices

Recognition of Urdu sign language: a systematic review of the machine learning classification

A computer vision-based system for recognition and classification of Urdu sign language dataset

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