Study of EMG-based Mouse Clicks Type Detection

The operation of a computer required humans to use several parts of their body. However, there were some conditions where humans cannot operate computers correctly or in a normal position; examples of these conditions were accident victims and people with disabilities. Therefore, a system was needed to help make it easier for these people to operate the computer. This study developed a system that can classify click types using EMG sensors, the K-NN method, and the SVM method. EMG sensors helped take data in the form of signals from human muscle contractions which will later be classified into left-click and right-click. At the same time, it was useful for classifying these types of clicks for the K-NN and SVM methods. Data from EMG sensors were trained using the K-NN and SVM methods using 54 data sets in each class, namely left-click and right-click classes. The K-NN method was trained using k=3, 5, 7, 9, and 11. The SVM method used linear kernels, Radial Basis Function (RBF), polynomials, and sigmoids. After that, the accuracy values of the two methods will be compared. The study has successfully classified the types of clicks based on the input from the EMG sensor using the K-NN method with the highest accuracy results using k=3, which was 81.81%, and the SVM method using polynomial kernels which were 84.84%. The highest accuracy value was obtained by comparing the two methods, namely using the polynomial kernel SVM method. Adding datasets and conducting experiments using other methods as further comparisons can be used to improve system accuracy.