Szczegóły publikacji
Opis bibliograficzny
Pattern recognition in the processing of electromyographic signals for selected expressions of Polish sign language / Anna Filipowska, Wojciech Filipowski, Julia Mieszczanin, Katarzyna Bryzik, Maciej Henkel, Emilia Skwarek, Paweł Raif, Szymon Sieciński, Rafał Doniec, Barbara Mika, Julia Bodak, Piotr Ferst, Marcin Pieniążek, Kamil Pilarski, Marcin Grzegorzek // Sensors [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 1424-8220 . — 2024 — vol. 24 iss. 20 art. no. 6710, s. 1-22. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 20-22, Abstr. — Publikacja dostępna online od: 2024-10-18. — Sz. Sieciński - afiliacja: Department of Clinical Engineering, Academy of Silesia, Katowice, Poland ; Institute of Medical Informatics, University of Luebeck, Lübeck, Germany
Autorzy (15)
- Filipowska Anna
- Filipowski Wojciech
- Mieszczanin Julia
- Bryzik Katarzyna
- Henkel Maciej
- Skwarek Emilia
- Raif Paweł
- Sieciński Szymon
- Doniec Rafał
- Mika Barbara
- Bodak Julia
- Ferst Piotr
- Pieniążek Marcin
- Pilarski Kamil
- Grzegorzek Marcin
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 164701 |
|---|---|
| Data dodania do BaDAP | 2026-01-23 |
| Tekst źródłowy | URL |
| DOI | 10.3390/s24206710 |
| Rok publikacji | 2024 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Sensors |
Abstract
Gesture recognition has become a significant part of human–machine interaction, particularly when verbal interaction is not feasible. The rapid development of biomedical sensing and machine learning algorithms, including electromyography (EMG) and convolutional neural networks (CNNs), has enabled the interpretation of sign languages, including the Polish Sign Language, based on EMG signals. The objective was to classify the game control gestures and Polish Sign Language gestures recorded specifically for this study using two different data acquisition systems: BIOPAC MP36 and MyoWare 2.0. We compared the classification performance of various machine learning algorithms, with a particular emphasis on CNNs on the dataset of EMG signals representing 24 gestures, recorded using both types of EMG sensors. The results (98.324% versus ≤7.8571% and 95.5307% versus ≤10.2697% of accuracy for CNNs and other classifiers in data recorded with BIOPAC MP36 and MyoWare, respectively) indicate that CNNs demonstrate superior accuracy. These results suggest the feasibility of using lower-cost sensors for effective gesture classification and the viability of integrating affordable EMG-based technologies into broader gesture recognition frameworks, providing a cost-effective solution for real-world applications. The dataset created during the study offers a basis for future studies on EMG-based recognition of Polish Sign Language.
