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

• Published in: Frontiers in human neuroscience Vol. 16; p. 911204
• Main Authors: Peng, Fulai, Chen, Cai, Lv, Danyang, Zhang, Ningling, Wang, Xingwei, Zhang, Xikun, Wang, Zhiyong
• Format: Journal Article
• Language: English
• Published: Lausanne Frontiers Research Foundation 16.06.2022; Frontiers Media S.A
• Subjects: Accuracy; Classification; Datasets; Deep learning; Discriminant analysis; Electromyography; Experiments; extreme learning machine; feature selection; gesture recognition; Human Neuroscience; Learning algorithms; Machine learning; Neural networks; sEMG signal; Support vector machines; Time series; Wavelet transforms
• ISSN: 1662-5161; 1662-5161
• DOI: 10.3389/fnhum.2022.911204

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 established using the selected features. Finally, the recognition results are determined by integrating the results obtained by ELM base classifiers using the majority voting method. The Ninapro DB5 dataset containing 52 different hand movements captured from 10 able-bodied subjects was used to evaluate the performance of the proposed method. The results showed that the proposed method could perform the best (overall average accuracy 77.9%) compared with decision tree (DT), ELM, and random forest (RF) methods.