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Using surface EMG signals is a non-invasive measurement method obtained as a result of muscle activity. In this study, surface EMG data have been used for classification, taken from healthy individuals or individuals with knee abnormalities in gait position. For this purpose, first feature extraction was realized by discrete wavelet transform from the data. Then, extracted features were classified by artificial neural network approach that is widely used in the literature. In classification process, artificial neural networks were trained by using simple cross-validation algorithm. During training the optimal network topology was determined. The highest classification performance of proposed model was obtained in rate fiction 80%-20% and 70%-30% of data set. Our results revealed that proposed artificial neural network model is able to detect knee abnormality from surface EMG signals. © 2017 IEEE.
| Yazar |
Erkaymaz, Okan Senyer, İrem Uzun, Rukiye |
| Yayın Türü | Proceedings |
| Tek Biçim Adres | https://hdl.handle.net/20.500.12628/5032 |
| Konu Başlıkları |
artificial neural networks
discrete wavelet transform surface Electromyography |
| Koleksiyonlar |
Fakülteler Mühendislik Fakültesi Bilgisayar Mühendisliği Bölümü Bildiri Koleksiyonu (Bilgisayar Mühendisliği Bölümü) Fakülteler Mühendislik Fakültesi Elektrik - Elektronik Mühendisliği Bölümü Bildiri Koleksiyonu (Elektrik - Elektronik Mühendisliği Bölümü) Araştırma Çıktıları | WoS | Scopus | TR-Dizin | PubMed | SOBİAD Scopus İndeksli Yayınlar Koleksiyonu |
| Dergi Adı | 2017 25th Signal Processing and Communications Applications Conference, SIU 2017 |
| Sayfalar | - |
| Yayın Yılı | 2017 |
| Eser Adı [dc.title] | Detection of knee abnormality from surface EMG signals by artificial neural networks |
| Yazar [dc.contributor.author] | Erkaymaz, Okan |
| Yazar [dc.contributor.author] | Senyer, İrem |
| Yazar [dc.contributor.author] | Uzun, Rukiye |
| Yayın Yılı [dc.date.issued] | 2017 |
| Yayıncı [dc.publisher] | Institute of Electrical and Electronics Engineers Inc. |
| Yayın Türü [dc.type] | proceedings |
| Açıklama [dc.description] | 25th Signal Processing and Communications Applications Conference, SIU 2017 -- 15 May 2017 through 18 May 2017 -- -- 128703 |
| Özet [dc.description.abstract] | Using surface EMG signals is a non-invasive measurement method obtained as a result of muscle activity. In this study, surface EMG data have been used for classification, taken from healthy individuals or individuals with knee abnormalities in gait position. For this purpose, first feature extraction was realized by discrete wavelet transform from the data. Then, extracted features were classified by artificial neural network approach that is widely used in the literature. In classification process, artificial neural networks were trained by using simple cross-validation algorithm. During training the optimal network topology was determined. The highest classification performance of proposed model was obtained in rate fiction 80%-20% and 70%-30% of data set. Our results revealed that proposed artificial neural network model is able to detect knee abnormality from surface EMG signals. © 2017 IEEE. |
| Kayıt Giriş Tarihi [dc.date.accessioned] | 2019-12-23 |
| Açık Erişim Tarihi [dc.date.available] | 2019-12-23 |
| Yayın Dili [dc.language.iso] | tur |
| Konu Başlıkları [dc.subject] | artificial neural networks |
| Konu Başlıkları [dc.subject] | discrete wavelet transform |
| Konu Başlıkları [dc.subject] | surface Electromyography |
| Haklar [dc.rights] | info:eu-repo/semantics/closedAccess |
| Alternatif Başlık [dc.title.alternative] | Yüzey EMG sinyallerinden diz anormalliğinin yapay sinir ağları ile tespiti |
| Dergi Adı [dc.relation.journal] | 2017 25th Signal Processing and Communications Applications Conference, SIU 2017 |
| Tek Biçim Adres [dc.identifier.uri] | https://dx.doi.org/10.1109/SIU.2017.7960160 |
| Tek Biçim Adres [dc.identifier.uri] | https://hdl.handle.net/20.500.12628/5032 |