Research Information System
Thesis Type: Postgraduate
Institution Of The Thesis: Cukurova University, Fen Bilimleri Enstitüsü, Elektrik-Elektronik Mühendisliği, Turkey
Approval Date: 2015
Thesis Language: English
Student: Durmuş Ali Eroğlu
Supervisor: Cabbar Veysel Baysal
Abstract:
The Surface Electromyographic (sEMG) signal is convenient for prosthetic
device control due to its non-invasive acquisition and its intrinsic
relation to the user's intention. This thesis presents an algorithm for
estimation of the real time elbow joint angle from sEMG signals acquired
from the muscles of biceps and triceps. The algorithm developed in the
thesis uses time-domain feature extraction methods such as mean absolute
value (MAV), root mean square (RMS) and waveform length (WL).
Estimation of the joint angle using extracted sEMG features is performed
by Artificial Neural Networks (ANN), specifically a Multilayer
Perceptron (MLP) and a general regression neural network (GRNN). The
overall system is implemented and tested in a real time hardware setup.
The results indicated that developed method could be successfully used
for a prosthetic arm device posture control.