Electromyography (EMG) signal recognition using combined discrete wavelet transform based on Artificial Neural Network (ANN)

Abstract

Rapid disability patients increasing over time and need a solution in the future. Hand amputation is one form of disability that common in Indonesian society. A possible solution would be necessary at the moment is the development of prosthetic hand that has the ability as a human hand. The development of neuroscience has now reached the stage of the body's ability to use the signal as an input signal to operate a system. One of the applications of the science development is the use of electromyography (EMG) signals as an input to the control system to operate the prosthetic hand. This study is divided into two stages: a preliminary study and further research. Initial research focus in the process of EMG signal pattern recognition and advanced research focus in the development of a prototype prosthetic hand that is integrated with the controller system. Preliminary research indicates that the results of pattern recognition EMG signal using wavelet transform and Artificial Neural Network (ANN) classification has an accuracy rate of about 77.5 %. Based on these results, it can be concluded that the study results could be used as a signal input to program control of the prosthetic hand that will be developed in phase two.

Wavelet Phase Coherence of Ictal Scalp EEG-Extracted Muscle Activity (SMA) as a Biomarker for Sudden Unexpected Death in Epilepsy (SUDEP)

“…Several methods for classification of EMG features have been reported. Empirical mode decomposition of EMG has been used to classify upper limb movements [18], while techniques based on discrete wavelet transforms of EMG have identified muscle movements [19][20][21].…”

EMG Sinyalleri Kullanılarak GoogLeNet ve Çok Seviyeli DPD ile El Tutma Hareketlerinin Sınıflandırılması

“…Çalışmada, 18-30 yaş arası 8 katılımcıdan elde edilen 303 EMG verisi kullanılarak dalgacık paket dönüşüm (DPD) yöntemi ile öznitelik çıkarımı gerçekleştirilmiştir. Arozi vd., EMG sinyallerinde hareket tespitini hedeflemişlerdir [11]. Bu hedefle alınan 8 farklı harekete ait EMG sinyallerine ADD uygulanmış ve buradan gelen katsayılara bazı istatistiksel yöntemler uygulanarak öznitelik belirlenmiştir.…”

Fraktal Eğimden Arındırılmış Dalgalılık Analizi ve Pencereli Kare Ortalamanın Karekökü Tabanlı EMG Sınıflandırma

“…Yapılan çalışma sonrasında sistemin doğruluk oranı %83 olarak belirlenmiştir. [7] nolu çalışmada EMG sinyallerinde hareket tespiti amaçlanmıştır bu amaçla alınan 8 farklı harekete ait EMG sinyallerine ADD uygulanmıştır ve buradan gelen katsayılara bazı istatistiksel metotlar uygulanarak öznitelik çıkarılmıştır. Çıkarılan bu verilerin Yapay Sinir Ağı (YSA) ile sınıflandırılmıştır.…”