EMG-Based Feature Extraction and Classification for Prosthetic Hand Control
This work addresses challenges in prosthetic hand control for amputees, representing an incremental improvement in feature extraction and postprocessing methods.
The paper tackled the problem of improving accuracy and reducing signal length for real-time EMG-based prosthetic hand control, achieving 95.5% accuracy with an 800 msec signal length.
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 approach 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 $95.5\%$ for $800$ msec signal length. The proposed postprocessing method achieves higher consistency compared with conventional majority voting and Bayesian fusion methods.