Pakistani Phrasal Sign Language Classification using Surface Electromyography

Khan, Muhammad Umar; Aziz, Sumair; Naqvi, Syed Zohaib Hassan; Amjad, Fatima; Shakeel, M. Saad

doi:10.1109/iccit-144147971.2020.9213806

Cited by 2 publications

(2 citation statements)

References 23 publications

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“…Table 2 depicts the details of all the datasets used in the selected studies, including a character-based, EMG signals-based, and sign images-based dataset. 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) .…”

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%

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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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Section: Resultsmentioning

confidence: 99%

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

View full text Add to dashboard Cite

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Sign Language Recognition Using the Electromyographic Signal: A Systematic Literature Review

Ben Haj Amor,

El Ghoul,

Jemni

2023

Sensors

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The analysis and recognition of sign languages are currently active fields of research focused on sign recognition. Various approaches differ in terms of analysis methods and the devices used for sign acquisition. Traditional methods rely on video analysis or spatial positioning data calculated using motion capture tools. In contrast to these conventional recognition and classification approaches, electromyogram (EMG) signals, which measure muscle electrical activity, offer potential technology for detecting gestures. These EMG-based approaches have recently gained attention due to their advantages. This prompted us to conduct a comprehensive study on the methods, approaches, and projects utilizing EMG sensors for sign language handshape recognition. In this paper, we provided an overview of the sign language recognition field through a literature review, with the objective of offering an in-depth review of the most significant techniques. These techniques were categorized in this article based on their respective methodologies. The survey discussed the progress and challenges in sign language recognition systems based on surface electromyography (sEMG) signals. These systems have shown promise but face issues like sEMG data variability and sensor placement. Multiple sensors enhance reliability and accuracy. Machine learning, including deep learning, is used to address these challenges. Common classifiers in sEMG-based sign language recognition include SVM, ANN, CNN, KNN, HMM, and LSTM. While SVM and ANN are widely used, random forest and KNN have shown better performance in some cases. A multilayer perceptron neural network achieved perfect accuracy in one study. CNN, often paired with LSTM, ranks as the third most popular classifier and can achieve exceptional accuracy, reaching up to 99.6% when utilizing both EMG and IMU data. LSTM is highly regarded for handling sequential dependencies in EMG signals, making it a critical component of sign language recognition systems. In summary, the survey highlights the prevalence of SVM and ANN classifiers but also suggests the effectiveness of alternative classifiers like random forests and KNNs. LSTM emerges as the most suitable algorithm for capturing sequential dependencies and improving gesture recognition in EMG-based sign language recognition systems.

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Pakistani Phrasal Sign Language Classification using Surface Electromyography

Cited by 2 publications

References 23 publications

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

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

Sign Language Recognition Using the Electromyographic Signal: A Systematic Literature Review

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