Hand gesture-based systems are one of the most effective technological advances. Surface electromyography (sEMG) is utilized as a popular input data for gesture classification with elevated accuracy and advanced control capability. In this thesis, which is based on the classification performance of Hilbert-Huang spectrum (HHS) images obtained from Hilbert Huang Transform (HHT) of the sEMG of the gestures, an evaluation of the results of artificial intelligence (AI) methods on hand gesture classification using HHS image is presented.
Eser Adı (dc.title) | Classification of Hand Gestures Using Time-Frequency Analysis and Different Artificial Intelligence Methods |
Eser Sahibi (dc.contributor.author) | Deniz Hande Kısa |
Tez Danışmanı (dc.contributor.advisor) | Onan GÜREN |
Yayıncı (dc.publisher) | İzmir Katip Çelebi Üniversitesi Fen Bilimleri Enstitüsü |
Tür (dc.type) | Yüksek Lisans |
Özet (dc.description.abstract) | Hand gesture-based systems are one of the most effective technological advances. Surface electromyography (sEMG) is utilized as a popular input data for gesture classification with elevated accuracy and advanced control capability. In this thesis, which is based on the classification performance of Hilbert-Huang spectrum (HHS) images obtained from Hilbert Huang Transform (HHT) of the sEMG of the gestures, an evaluation of the results of artificial intelligence (AI) methods on hand gesture classification using HHS image is presented. |
Kayıt Giriş Tarihi (dc.date.accessioned) | 2023-08-08 |
Açık Erişim Tarihi (dc.date.available) | 2024-02-08 |
Yayın Tarihi (dc.date.issued) | 2023 |
Yayın Dili (dc.language.iso) | eng |
Konu Başlıkları (dc.subject) | Hand gestures |
Konu Başlıkları (dc.subject) | time-frequency |
Tek Biçim Adres (dc.identifier.uri) | https://hdl.handle.net/11469/3520 |