EMG Signal Classification Using Reflection Coefficients and Extreme Value Machine
This is an incremental improvement for EMG-based gesture recognition systems.
The paper tackled EMG signal classification for gesture recognition by using reflection coefficients from an Autoregressive model and Extreme Value Machine, achieving better accuracy compared to KNN and SVM.
Electromyography is a promising approach to the gesture recognition of humans if an efficient classifier with high accuracy is available. In this paper, we propose to utilize Extreme Value Machine (EVM) as a high-performance algorithm for the classification of EMG signals. We employ reflection coefficients obtained from an Autoregressive (AR) model to train a set of classifiers. Our experimental results indicate that EVM has better accuracy in comparison to the conventional classifiers approved in the literature based on K-Nearest Neighbors (KNN) and Support Vector Machine (SVM).