  The main purpose of this research is to move the robotic arm (5DoF) in
real-time, based on the surface Electromyography (sEMG) signals, as obtained
from the wireless Myo gesture armband to distinguish seven hand movements. The
sEMG signals are biomedical signals that estimate and record the electrical
signals produced in muscles through their contraction and relaxation,
representing neuromuscular activities. Therefore, controlling the robotic arm
via the muscles of the human arm using sEMG signals is considered to be one of
the most significant methods. The wireless Myo gesture armband is used to
record sEMG signals from the forearm. In order to analyze these signals, the
pattern recognition system is employed, which consists of three main parts:
segmentation, feature extraction, and classification. Overlap technique is
chosen for segmenting part of the signal. Six time domain features (MAV, WL,
RMS, AR, ZC, and SSC) are extracted from each segment. The classifiers (SVM,
LDA, and KNN) are employed to enable comparison between them in order to obtain
optimum accuracy of the system. The results show that the SVM achieves higher
system accuracy at 96.57 %, compared to LDA reaching 96.01 %, and 92.67 %
accuracy achieved by KNN.
