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Title: Ampute elektromiyografi sinyallerinin evrişimli sinir ağları kullanılarak sınıflandırılması
Authors: Onay, Fatih
Mert, Ahmet
Keywords: Electromyography
Convolutional neural networks
Pattern recognition
Issue Date: 2020
Publisher: IEEE
Abstract: The classification of EMG signals for the amputees is important to develop a powered-prosthetic that is capable of replacing with lost limbs. The EMG signals collected from residual limbs reduce the classification accuracy due to muscle movements that cannot be realized properly. In this study, classification performance is aimed to be increased by combining CNN with root mean square (RMS) and waveform length (WL) that are used in analysis of EMG signals successfully. The features such as RMS and WL extracted from EMG signals for the classification of six hand movements at the low, medium, and high force levels were applied to CNN input, and classification results were compared with nearest neighbour and linear discriminant analysis.
Description: 2020 Medical Technologies Congress (TIPTEKNO) -- NOV 19-20, 2020 -- ELECTR NETWORK -- Biyomedikal ve Klinik Muhendisligi Dernegi, Izmir Ekonomi Univ, Izmir Katip Celebi Univ
ISBN: 978-1-7281-8073-1
Appears in Collections:Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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