Application of Wavelet Decomposition and Machine Learning for the sEMG Signal Based Gesture Recognition

Abstract

The amputees throughout the world have limited access to the high-quality intelligent prostheses. The correct recognition of gestures is one of the most difficult tasks in the context of surface electromyography (sEMG) based prostheses development. This chapter shows a comparative examination of the several machine learning-based algorithms for the hand gestures identification. The first step in the process is the data extraction from the sEMG device, followed by the features extraction. Then, the two robust machine learning algorithms are applied to the extracted feature set to compare their prediction accuracy. The medium Gaussian Support Vector Machine (SVM) performs better under all conditions as compared to the K-nearest neighbor. Different parameters are used for the performance comparison which include F1 score, accuracy, precision and Kappa index. The proposed method of hand gesture recognition, based on sEMG, is thoroughly investigated and the results have shown a promising performance. In any case, the miscalculation during feature extraction can reduce the recognition precision. The profound learning technique are used to achieve a high precision. Therefore, the proposed design takes into account all aspects while processing the sEMG signal. The system secures a highest classification accuracy of 92.2% for the case of Gaussian SVM algorithm.