Finger Movement Recognition using Machine Learning Algorithms with Tree-Seed Algorithm

Electromyography (EMG) signals have been used to recognize various actions of hand movements, finger movements, and hand gestures. This paper aims to improve the classification accuracy of EMG signals while decreasing the number of features using the Tree-Seed Algorithm. The dataset containing EMG signals utilized in this investigation is derived from a publicly accessible source. The rationale for selecting the Tree-Seed Algorithm centers on its ability to enhance classification accuracy while minimizing the dimensionality of feature sets. The object function and Tree-Seed Algorithm's nature avoids the results to have low accuracy with fewer features. The aim is not just to use a smaller number of features but also to gain a higher accuracy rate. To ensure selecting a smaller number of features does not decrease the classification accuracy, the performances of all feature subsets were controlled with the object function. As a result, the number of selected features have decreased and the accuracy rate have been increased. The best accuracy rate change have observed as 84.78\% to 90.21\% using K-Nearest Neighbor (kNN) classifier with 50 features of 80 feature. And the maximum classification accuracy rate achieved as 99.75\% using kNN classifier. In this study, two different feature sets have been compared with two different optimization algorithms using four different traditional machine learning algorithms to compare the classification accuracy change. All of the classification accuracy and classification accuracy increase results have been reported with the amount of selected features at the end of iterations.