Sign Language Recognition Using the Electromyographic Signal: A Systematic Literature Review
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 usin...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/19/8343 |
| _version_ | 1797575175360741376 |
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| author | Amina Ben Haj Amor Oussama El Ghoul Mohamed Jemni |
| author_facet | Amina Ben Haj Amor Oussama El Ghoul Mohamed Jemni |
| author_sort | Amina Ben Haj Amor |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T21:34:55Z |
| format | Article |
| id | doaj.art-b0153b2f274a4879bde74df1312adefc |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T21:34:55Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-b0153b2f274a4879bde74df1312adefc2023-11-19T15:06:14ZengMDPI AGSensors1424-82202023-10-012319834310.3390/s23198343Sign Language Recognition Using the Electromyographic Signal: A Systematic Literature ReviewAmina Ben Haj Amor0Oussama El Ghoul1Mohamed Jemni2Research Laboratory LaTICE, University of Tunis, Tunis 1008, TunisiaMada—Assistive Technology Center Qatar, Doha P.O. Box 24230, QatarArab League Educational, Cultural, and Scientific Organization, Tunis 1003, TunisiaThe 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.https://www.mdpi.com/1424-8220/23/19/8343sign language recognitionsystematic reviewsEMGelectromyographic signal |
| spellingShingle | Amina Ben Haj Amor Oussama El Ghoul Mohamed Jemni Sign Language Recognition Using the Electromyographic Signal: A Systematic Literature Review Sensors sign language recognition systematic review sEMG electromyographic signal |
| title | Sign Language Recognition Using the Electromyographic Signal: A Systematic Literature Review |
| title_full | Sign Language Recognition Using the Electromyographic Signal: A Systematic Literature Review |
| title_fullStr | Sign Language Recognition Using the Electromyographic Signal: A Systematic Literature Review |
| title_full_unstemmed | Sign Language Recognition Using the Electromyographic Signal: A Systematic Literature Review |
| title_short | Sign Language Recognition Using the Electromyographic Signal: A Systematic Literature Review |
| title_sort | sign language recognition using the electromyographic signal a systematic literature review |
| topic | sign language recognition systematic review sEMG electromyographic signal |
| url | https://www.mdpi.com/1424-8220/23/19/8343 |
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