Yıl: 2023 Cilt: 27 Sayı: 1 Sayfa Aralığı: 214 - 225 Metin Dili: İngilizce DOI: 10.16984/saufenbilder.1176459 İndeks Tarihi: 13-03-2023

Using Wavelet Analysis and Deep Learning for EMG-Based Hand Movement Signal Classification

Öz:
In this study; time series electromyography (EMG) data have been classified according to hand movements using wavelet analysis and deep learning. A pre-trained deep CNN (Convolitonal Neural Network-GoogLeNet) has been used in the classification process performed with signal processing, by this way the results can be obtained by continuous wavelet transform and classification methods. The dataset used has been taken from the Machine Learning Repository at the University of California. In the data set; EMG data of 5 healthy individuals, 2 males and 3 females, of the same age (~20-22 years) are available. Data; It consists of grasping spherical objects (Spher), grasping small objects with fingertips (Tip), grasping objects with palms (Palm), grasping thin/flat objects (Lat), grasping cylindrical objects (Cyl) and holding heavy objects (Hook). It is desired to perform 6 hand movements at the same time. While these movements are necessary, speed and power depend on one's will. People perform each movement for 6 seconds and repeat each movement (action) 30 times. The CWT (Continuous Wavelet Transform) method was used to transform the signal into an image. The scalogram image of the signal was created using the CWT method and the generated images were collected in a data set folder. The collected scalogram images have been classified using GoogLeNet, a deep learning network model. With GoogLeNet, results with 97.22% and 88.89% accuracy rates were obtained by classifying the scalogram images of the signals received separately from channel 1 and channel 2 in the data set. The applied model can be used to classify EMG signals in EMG data with high success rate. In this study, 80% of data was used for educational purposes and 20% for validation purposes. In the study, the results of the classification processes have been evaluated separately for first and second channel data.
Anahtar Kelime: Deep learning continuous wavelet transform (CWT) skalogram electromyography (EMG) GoogLeNet

Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık
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APA GÜNES H, AKKAYA A (2023). Using Wavelet Analysis and Deep Learning for EMG-Based Hand Movement Signal Classification. , 214 - 225. 10.16984/saufenbilder.1176459
Chicago GÜNES HARUN,AKKAYA Abdullah Erhan Using Wavelet Analysis and Deep Learning for EMG-Based Hand Movement Signal Classification. (2023): 214 - 225. 10.16984/saufenbilder.1176459
MLA GÜNES HARUN,AKKAYA Abdullah Erhan Using Wavelet Analysis and Deep Learning for EMG-Based Hand Movement Signal Classification. , 2023, ss.214 - 225. 10.16984/saufenbilder.1176459
AMA GÜNES H,AKKAYA A Using Wavelet Analysis and Deep Learning for EMG-Based Hand Movement Signal Classification. . 2023; 214 - 225. 10.16984/saufenbilder.1176459
Vancouver GÜNES H,AKKAYA A Using Wavelet Analysis and Deep Learning for EMG-Based Hand Movement Signal Classification. . 2023; 214 - 225. 10.16984/saufenbilder.1176459
IEEE GÜNES H,AKKAYA A "Using Wavelet Analysis and Deep Learning for EMG-Based Hand Movement Signal Classification." , ss.214 - 225, 2023. 10.16984/saufenbilder.1176459
ISNAD GÜNES, HARUN - AKKAYA, Abdullah Erhan. "Using Wavelet Analysis and Deep Learning for EMG-Based Hand Movement Signal Classification". (2023), 214-225. https://doi.org/10.16984/saufenbilder.1176459
APA GÜNES H, AKKAYA A (2023). Using Wavelet Analysis and Deep Learning for EMG-Based Hand Movement Signal Classification. Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 27(1), 214 - 225. 10.16984/saufenbilder.1176459
Chicago GÜNES HARUN,AKKAYA Abdullah Erhan Using Wavelet Analysis and Deep Learning for EMG-Based Hand Movement Signal Classification. Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi 27, no.1 (2023): 214 - 225. 10.16984/saufenbilder.1176459
MLA GÜNES HARUN,AKKAYA Abdullah Erhan Using Wavelet Analysis and Deep Learning for EMG-Based Hand Movement Signal Classification. Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi, vol.27, no.1, 2023, ss.214 - 225. 10.16984/saufenbilder.1176459
AMA GÜNES H,AKKAYA A Using Wavelet Analysis and Deep Learning for EMG-Based Hand Movement Signal Classification. Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi. 2023; 27(1): 214 - 225. 10.16984/saufenbilder.1176459
Vancouver GÜNES H,AKKAYA A Using Wavelet Analysis and Deep Learning for EMG-Based Hand Movement Signal Classification. Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi. 2023; 27(1): 214 - 225. 10.16984/saufenbilder.1176459
IEEE GÜNES H,AKKAYA A "Using Wavelet Analysis and Deep Learning for EMG-Based Hand Movement Signal Classification." Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 27, ss.214 - 225, 2023. 10.16984/saufenbilder.1176459
ISNAD GÜNES, HARUN - AKKAYA, Abdullah Erhan. "Using Wavelet Analysis and Deep Learning for EMG-Based Hand Movement Signal Classification". Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi 27/1 (2023), 214-225. https://doi.org/10.16984/saufenbilder.1176459