Surface Electromyography based Pakistani Sign Language Interpreter

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

doi:10.1109/icecce49384.2020.9179254

Cited by 5 publications

(3 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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%

See 1 more Smart Citation

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

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.

show abstract

Section: Resultsmentioning

confidence: 99%

“…Another lack of concern that the author has is the lackness of machine learning outcomes, i.e., specificity and sensitivity. Only one study, i.e., Khan et al (2020a) , reports all three products: accuracy, specificity, and sensitivity. Naseem et al (2019) didn’t write the accuracy, the primary machine learning outcome.…”

Section: Discussionmentioning

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

show abstract

“…A recent study by Khan et al. (2020) showed Electromyography (sEMG) technique for the detection of electrical signals that are produced as a result of hand movements for various alphabetical signs of PSL.…”

Section: Literature Reviewmentioning

confidence: 99%

Pakistan sign language translation tool in educational setting: teachers perspective

Abbas

Sarfraz

Tariq

2022

JET

View full text Add to dashboard Cite

PurposeThe current study aims to determine the viability of the tool developed by Abbas and Sarfraz (2018) to translate English speech and text to Pakistan Sign Language (PSL) with bilingual subtitles.Design/methodology/approachFocus group interviews of 30 teachers of a Pakistani private university were conducted; who used the PSL translation tool in their classrooms for lecture delivery and communication with the deaf students.FindingsThe findings of the study determined the viability of the developed tool and showed that it is helpful in teaching deaf students efficiently. With the availability of this tool, teachers are not dependent on human sign language (SL) interpreters in their classrooms.Originality/valueOverall, this tool is an effective addition to educational technology for special education. Due to the lack of Sign Language (SL) understanding, learning resources and availability of human SL interpreters in Pakistan, institutions feel dependency and scarcity to educate deaf students in a classroom. Unimpaired people and especially teachers face problems communicating with deaf people to arrange one interpreter for a student(s) in multiple classes at the same time which creates a communication gap between a teacher and a deaf student.

show abstract

Supervised Machine Learning based Fast Hand Gesture Recognition and Classification Using Electromyography (EMG) Signals

Khan

Muneeb

et al. 2021

2021 International Conference on Applied and Engineering Mathematics (ICAEM)

View full text Add to dashboard Cite

Surface Electromyography based Pakistani Sign Language Interpreter

Cited by 5 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

Pakistan sign language translation tool in educational setting: teachers perspective

Supervised Machine Learning based Fast Hand Gesture Recognition and Classification Using Electromyography (EMG) Signals

Contact Info

Product

Resources

About