The recent revolution in the biomedical field carried out the researchers to work on the prosthetic technique
because it reflects the amputee's need. Therefore, the electromyography (EMG) signals generated by muscle
contractions are used to implement the prosthetic human body parts. This paper presents a pattern recognition system
based on two EMG data; the first EMG data represents the general body movements collected from biceps and triceps
muscles for six different motions, i.e., bowing, clapping, handshaking, hugging, jumping, and running. The Root Mean
Square, Difference Absolute Standard Deviation Value, and Principal Component Analysis extract the raw signal data
features and enhance classification accuracy. The machine learning method is applied, i.e., Support Vector Machine
(SVM) and K-Nearest Neighbours (KNN) are used for data classification; the results show high accuracy reached
94.8% and 98.9%, respectively. Whereas, the second EMG data is selected to be more specific in hand movements,
i.e., cylindrical, spherical, palmar, lateral, hook, and tip motions, because these significant motions are the first step
implementing any prosthetic hand. Consequently, the mean, Standard Deviation Value, and Principal Component
Analysis extract the raw signal feature. Meanwhile, the same algorithm used in the first data classification is also used
to classify the second data because it shows high accuracy and good performance. SVM algorithm is used to classify
the data and achieved high training accuracy, reaching 89%. The high training accuracy for different hand movements
is considered an essential step toward implementing human prosthetic parts to help the people who suffer from an
amputee.