Gesture Recognition by Ensemble Extreme Learning Machine Based on Surface Electromyography Signals.

ABSTRACT: In the recent years, gesture recognition based on the surface electromyography (sEMG) signals has been extensively studied. However, the accuracy and stability of gesture recognition through traditional machine learning algorithms are still insufficient to some actual application scenarios. To enhance this situation, this paper proposed a method combining feature selection and ensemble extreme learning machine (EELM) to improve the recognition performance based on sEMG signals. First, the input sEMG signals are preprocessed and 16 features are then extracted from each channel. Next, features that mostly contribute to the gesture recognition are selected from the extracted features using the recursive feature elimination (RFE) algorithm. Then, several independent ELM base classifiers are es

SUBMITTER: Peng F 

PROVIDER: S-EPMC9243223 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature