OPTIMIZATION METHOD FOR GESTURE CLASSIFIER

Abstract

The work is devoted to the study of the possibility of optimizing the process of synthesis of gesture classifiers by selecting the most significant channels of electromyographic (EMG) activity of the muscles of the forearm. The first part of the study is devoted to the development and analysis of the performance of gesture classifiers with a different number of EMG channels, ranked by significance based on the Pearson criterion. The solution of the problem of classification of gestures by EMG signals was first implemented on the basis of ensembles of decision trees trained by the gradient boosting method. For this, software was developed that allows automatic synthesis and training of gesture classifiers. Next, a series of studies was carried out to find the optimal number of EMG channels based on three criteria: the classifier learning rate, the performance of the trained model, and the area under the ROC AUC error curve. To do this, a cycle of training and testing of the classifier was carried out for data sets recorded at different positions of the electrodes on the forearm. Then, range diagrams of the studied criteria were constructed for various numbers of EMG channels involved in the work from 1 to 8, ranked by significance in each of the samples. It was found that the optimal number of EMG channels involved under the experimental conditions was 3-6, since a further increase did not lead to a decrease in the classification error, while significantly degrading the performance. The proposed method allows you to automatically select the channels, the electrodes of which are located above the most informative areas of the forearm in case of an accidental change in the position of the sensors. The second part of the work contains the results of a full-scale experiment to demonstrate the possibility of controlling a wheeled robot through EMG analysis.