Deep Convolutional Spiking Neural Network Based Hand Gesture Recognition

被引：2
|
作者：
Ke, Weijie [1 ]
Xing, Yannan [1 ]
Di Caterina, Gaetano [1 ]
Petropoulakis, Lykourgos [1 ]
Soraghan, John [1 ]
机构：
[1] Univ Strathclyde, Elect & Elect Engn, Glasgow, Lanark, Scotland
来源：
2020 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) | 2020年
关键词：
convolutional spiking neural network; gesture recognition; EMG; signal processing; EMG; INFORMATION; PROSTHESES;
D O I：
10.1109/ijcnn48605.2020.9207040
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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.
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收藏
页数：7
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