Deep convolutional spiking neural network based hand gesture recognition

Ke, Weijie and Xing, Yannan and Di Caterina, Gaetano and Petropoulakis, Lykourgos and Soraghan, John; (2020) Deep convolutional spiking neural network based hand gesture recognition. In: 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, GBR. ISBN 9781728169262 (https://doi.org/10.1109/IJCNN48605.2020.9207040)

[thumbnail of Ke-etal-WCCI-2020-Deep-convolutional-spiking-neural-network-based-hand-gesture-recognition]
Preview
 Text. Filename: Ke_etal_WCCI_2020_Deep_convolutional_spiking_neural_network_based_hand_gesture_recognition.pdf
Accepted Author Manuscript
Download (1MB)| Preview

Abstract

Novel technologies for EMG (Electromyogram) based hand gesture recognition have been investigated for many industrial applications. In this paper, a novel approach which is based on a specific designed spiking convolution neural network which is fed by a novel EMG signal energy density map is presented. The experimental results indicate that the new approach not only rapidly decreases the required processing time but also increases the average recognition accuracy to 98.76% based on the Strathclyde dataset and to 98.21% based on the CapgMyo open source dataset. A relative comparison of experimental results between the proposed novel EMG based hand gesture recognition methodology and other similar approaches indicates the superior effectiveness of the new design.

ORCID iDs

Ke, Weijie, Xing, Yannan, Di Caterina, Gaetano ORCID logoORCID: https://orcid.org/0000-0002-7256-0897, Petropoulakis, Lykourgos ORCID logoORCID: https://orcid.org/0000-0003-3230-9670 and Soraghan, John ORCID logoORCID: https://orcid.org/0000-0003-4418-7391;
CORE (COnnecting REpositories)