Deep convolutional spiking neural network based hand gesture recognition

Weijie Ke; Yannan Xing; Gaetano Di Caterina; Lykourgos Petropoulakis; John Soraghan

doi:10.1109/IJCNN48605.2020.9207040

Deep convolutional spiking neural network based hand gesture recognition

Weijie Ke, Yannan Xing, Gaetano Di Caterina, Lykourgos Petropoulakis, John Soraghan

Electronic And Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

3 Citations (Scopus)

31 Downloads (Pure)

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.

Original language	English
Title of host publication	2020 International Joint Conference on Neural Networks (IJCNN)
Place of Publication	Piscataway, NJ.
Publisher	IEEE
Number of pages	7
ISBN (Electronic)	9781728169262
ISBN (Print)	9781728169279
DOIs	https://doi.org/10.1109/IJCNN48605.2020.9207040
Publication status	Published - 28 Sep 2020
Event	2020 International Joint Conference on Neural Networks (IJCNN)- IEEE World congress on computational intelligence(WCCI) 2020: IJCNN - glasgow, Glasgow, United Kingdom Duration: 19 Jul 2020 → 24 Jul 2020 Conference number: 48605X https://wcci2020.org/ijcnn-sessions/ https://wcci2020.org/

Conference

Conference	2020 International Joint Conference on Neural Networks (IJCNN)- IEEE World congress on computational intelligence(WCCI) 2020
Abbreviated title	IJCNN
Country/Territory	United Kingdom
City	Glasgow
Period	19/07/20 → 24/07/20
Internet address	https://wcci2020.org/ijcnn-sessions/ https://wcci2020.org/

Keywords

Spiking Neural network
Convolutional neural network
gesture recognition
EMG signal processing

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10.1109/IJCNN48605.2020.9207040

Ke-etal-WCCI-2020-Deep-convolutional-spiking-neural-network-based-hand-gesture-recognitionAccepted author manuscript, 1.56 MB

Cite this

@inproceedings{cc37f20a1d2a4ee28ebf66b504b35b19,

title = "Deep convolutional spiking neural network based hand gesture recognition",

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.",

keywords = "Spiking Neural network, Convolutional neural network, gesture recognition, EMG signal processing",

author = "Weijie Ke and Yannan Xing and {Di Caterina}, Gaetano and Lykourgos Petropoulakis and John Soraghan",

note = "{\textcopyright} 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.; 2020 International Joint Conference on Neural Networks (IJCNN)- IEEE World congress on computational intelligence(WCCI) 2020 : IJCNN, IJCNN ; Conference date: 19-07-2020 Through 24-07-2020",

year = "2020",

month = sep,

day = "28",

doi = "10.1109/IJCNN48605.2020.9207040",

language = "English",

isbn = "9781728169279",

booktitle = "2020 International Joint Conference on Neural Networks (IJCNN)",

publisher = "IEEE",

url = "https://wcci2020.org/ijcnn-sessions/, https://wcci2020.org/",

}

Ke, W, Xing, Y, Di Caterina, G, Petropoulakis, L & Soraghan, J 2020, Deep convolutional spiking neural network based hand gesture recognition. in 2020 International Joint Conference on Neural Networks (IJCNN)., 9207040, IEEE, Piscataway, NJ., 2020 International Joint Conference on Neural Networks (IJCNN)- IEEE World congress on computational intelligence(WCCI) 2020, Glasgow, United Kingdom, 19/07/20. https://doi.org/10.1109/IJCNN48605.2020.9207040

TY - GEN

T1 - Deep convolutional spiking neural network based hand gesture recognition

AU - Ke, Weijie

AU - Xing, Yannan

AU - Di Caterina, Gaetano

AU - Petropoulakis, Lykourgos

AU - Soraghan, John

N1 - Conference code: 48605X

PY - 2020/9/28

Y1 - 2020/9/28

N2 - 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.

AB - 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.

KW - Spiking Neural network

KW - Convolutional neural network

KW - gesture recognition

KW - EMG signal processing

UR - https://ieeexplore.ieee.org/document/9207040

U2 - 10.1109/IJCNN48605.2020.9207040

DO - 10.1109/IJCNN48605.2020.9207040

M3 - Conference contribution book

SN - 9781728169279

BT - 2020 International Joint Conference on Neural Networks (IJCNN)

PB - IEEE

CY - Piscataway, NJ.

T2 - 2020 International Joint Conference on Neural Networks (IJCNN)- IEEE World congress on computational intelligence(WCCI) 2020

Y2 - 19 July 2020 through 24 July 2020

ER -

Deep convolutional spiking neural network based hand gesture recognition

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