EMG-Based Feature Extraction and Classification for Prosthetic Hand Control

Abstract

In recent years, real-time control of prosthetic hands has gained a great deal of attention. In particular, real-time analysis of Electromyography (EMG) signals has several challenges to achieve an acceptable accuracy and execution delay. In this paper, we address some of these challenges by improving the accuracy in a shorter signal length. We first introduce a set of new feature extraction functions applying on each level of wavelet decomposition. Then, we propose a postprocessing ap- proach to process the neural network outputs. The experimental results illustrate that the proposed method enhances the accuracy of real-time classification of EMG signals up to 95.5 percent for 800 msec signal length. The proposed postprocessing method achieves higher consistency compared with conventional majority voting and Bayesian fusion methods.

Feature Extraction Based on Sparse Coding Approach for Hand Grasp Type Classification

“…For example, most feature extraction techniques are specific to hand kinematic datasets derived from hand image datasets. Additionally, some techniques are only suitable for specific hand analyses using EMG signals [24,25] and are difficult to extend to other types of hand kinematic data. Moreover, most of the traditional feature extraction methods are focused on extracting features based on subspace-learningbased approaches while not adequately exploiting the consistency and complementary information among the other types of hand kinematic data, especially hand kinematic time-series formats.…”