Karapinar Senturk, ZehraBakay, Melahat Sevgul2021-12-012021-12-0120212255-2863https://doi.org/10.14201/ADCAIJ2021102123136https://hdl.handle.net/20.500.12684/10024Gestures are one of the most important agents for human-computer interaction. They play a mediator role between human intention and the control of machines. Electromyography (EMG) pattern recognition has been studied for gesture recognition for years to control of prostheses and rehabilitation systems. EMG data gives information about the electrical activity related to muscles. It is obtained from the arm and helps to understand hand gestures. For this work, hand gesture data taken from UCI2019 EMG dataset obtained from myo Thalmic armband were classified with six different machine learning algorithms. Artificial Neural Network (ANN), Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), Naive Bayes (NB), Decision Tree (DT) and Random Forest (RF) methods were applied for comparison based on several performance metrics which are accuracy, precision, sensitivity, specificity, classification error, kappa, root mean squared error (RMSE) and correlation. The data belongs to seven hand gestures. 700 samples from 7 classes (100 samples per group) were used in the experiments. Splitting ratio in the classification was 0.8-0.2, i.e. 80% of the samples were used in training and 20% of data were used in the testing phase of the classifier. NB was found to be the best among other methods because of high accuracy. Classification accuracy varies between 97.52% to 100% for each gesture. Considering the results of the performance metrics, it can be said that this study recognizes and classifies seven hand gestures successfully in comparison with the literature. The proposed method can easily be used for human-machine interaction and smart device controlling like prosthesis, wheelchair, and smart entertainment applications.en10.14201/ADCAIJ2021102123136info:eu-repo/semantics/closedAccessEMGmyo armbandhand gesture recognitionmachine learningSchemeArticle102123136WOS:000704721300002N/A