On the Use of Deeper CNNs in Hand Gesture Recognition Based on sEMG Signals

Abstract

In the past few years, a great interest for the classification of hand gestures with Deep Learning methods based on surface electromyography (sEMG) signals has been developed in the scientific community. In line with latest works in the field, the objective of our work is the construction of a novel Convolutional Neural Network architecture, for the classification of hand-gestures. Our model, while avoiding overfitting, did not perform significantly better compared to a much shallower network. The results suggest that the lack of diversity in the sEMG recordings between certain hand-gestures limits the performance of the model. In addition, the classification accuracy on a database we developed using a commercial device (Myo Armband) was substantially higher (approximately 24%) than a similar benchmark dataset recorded with the same device.