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Depot Institutionnel de l'UMBB >
Mémoires de Master 2 >
Institut de Génie Electrique et d'Electronique >
Computer >
Veuillez utiliser cette adresse pour citer ce document :
http://dlibrary.univ-boumerdes.dz:8080/handle/123456789/11929
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| Titre: | EMG-based Hand Gesture Classification Using Deep Learning |
| Auteur(s): | Halzoun, Maya
Boutellaa, Elhocine (Supervisor) |
| Mots-clés: | Deep Learning methods
Convolutional Neural Network. |
| Date de publication: | 2022 |
| Résumé: | In recent years, Deep Learning methods have been successfully applied to a wide range
of image and speech recognition problems highly impacting other research fields. As a result,
new works in biomedical engineering are directed towards the application of these methods to
electromyography-based gesture recognition. In this report, we present a brief overview of Deep
Learning methods for electromyography-based hand gesture recognition along with a
comparison between different deep learning architectures. We used four architectures that are:
CNN, RNN, LSTM and hybrid CNN-LSTM. The proposed networks yield to various levels of
accuracy depending on the used model, including that our best model was the CNN model
which resulted in the highest accuracy. The proposed analysis helps in understanding the
limitations of the model and exploring new ways to improve the performance. |
| Description: | 57p. |
| URI/URL: | http://dlibrary.univ-boumerdes.dz:8080/handle/123456789/11929 |
| Collection(s) : | Computer
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