A Convolutional Neural Network With Multi-scale Kernel and Feature Fusion for sEMG-based Gesture Recognition

The sEMG-based gesture recognition has great potential in human-computer interaction. However, the current approaches are far from optimal. In this paper, a novel convolutional neural network which combines the multi-scale kernal and feature fusion (MKFF-CNN) is proposed. This model could extract multi-scale features and make full use of these feature maps. One dataset called "gForce dataset" is recorded in this work, comprising 13 able-bodied participants. To verify the effectiveness of the proposed model, evaluations on both the gForce dataset and the Ninapro DB6 are conducted. The experimental results demonstrate that, MKFF-CNN achieves an accuracy of 97.65% on the gForce dataset, which is 1.39% higher than that of the subnetwork without early fusion, and at least 6.54% higher than that of the subnetwork with single-scale convolution kernel. On the Ninapro DB6, MKFF-CNN achieves 98.52% accuracy, which is 1.3% higher than the state-of-the-art works, showing the superiority of the proposed model.