Novel Wearable System to Recognize Sign Language in Real Time

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dc.authoridKUMDERELI, UMIT CAN/0000-0002-9413-942X
dc.authoridumut, ilhan/0000-0002-5269-1128
dc.contributor.authorUmut, Ilhan
dc.contributor.authorKumdereli, Umit Can
dc.date.accessioned2024-10-29T17:59:26Z
dc.date.available2024-10-29T17:59:26Z
dc.date.issued2024
dc.departmentTekirdağ Namık Kemal Üniversitesi
dc.description.abstractThe aim of this study is to develop a practical software solution for real-time recognition of sign language words using two arms. This will facilitate communication between hearing-impaired individuals and those who can hear. We are aware of several sign language recognition systems developed using different technologies, including cameras, armbands, and gloves. However, the system we propose in this study stands out for its practicality, utilizing surface electromyography (muscle activity) and inertial measurement unit (motion dynamics) data from both arms. We address the drawbacks of other methods, such as high costs, low accuracy due to ambient light and obstacles, and complex hardware requirements, which have limited their practical application. Our software can run on different operating systems using digital signal processing and machine learning methods specific to this study. For the test, we created a dataset of 80 words based on their frequency of use in daily life and performed a thorough feature extraction process. We tested the recognition performance using various classifiers and parameters and compared the results. The random forest algorithm emerged as the most successful, achieving a remarkable 99.875% accuracy, while the na & iuml;ve Bayes algorithm had the lowest success rate with 87.625% accuracy. The new system promises to significantly improve communication for people with hearing disabilities and ensures seamless integration into daily life without compromising user comfort or lifestyle quality.
dc.description.sponsorshipThe authors acknowledge the support given by Hulya AYKUTLU in providing them with knowledge.
dc.identifier.doi10.3390/s24144613
dc.identifier.issn1424-8220
dc.identifier.issue14en_US
dc.identifier.pmid39066011
dc.identifier.scopus2-s2.0-85199796034
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3390/s24144613
dc.identifier.urihttps://hdl.handle.net/20.500.11776/14737
dc.identifier.volume24
dc.identifier.wosWOS:001277543200001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofSensors
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectartificial intelligence
dc.subjectcomputer software
dc.subjecthuman-computer interaction
dc.subjectinertial measurement unit
dc.subjectsign language recognition
dc.subjectsurface electromyography
dc.titleNovel Wearable System to Recognize Sign Language in Real Time
dc.typeArticle

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