Intersected EMG heatmaps and deep learning based gesture recognition

Research output: Contribution to conferencePaper

Abstract

Hand gesture recognition in myoelectric based prosthetic devices is a key challenge to offering effective solutions to hand/lower arm amputees. A novel hand gesture recognition methodology that employs the difference of EMG energy heatmaps as the input of a specific designed deep learning neural network is presented. Experimental results using data from real amputees indicate that the proposed design achieves 94.31% as average accuracy with best accuracy rate of 98.96%. A comparison of experimental results between the proposed novel hand gesture recognition methodology and other similar approaches indicates the superior effectiveness of the new design.
Original languageEnglish
Number of pages6
Publication statusAccepted/In press - 10 Oct 2019
Event12th International Conference on Machine Learning and Computing - Shenzhen, China
Duration: 15 Feb 202017 Feb 2020

Conference

Conference12th International Conference on Machine Learning and Computing
Abbreviated titleICMLC 2020
CountryChina
CityShenzhen
Period15/02/2017/02/20

Fingerprint

Gesture recognition
Prosthetics
Neural networks
Deep learning

Keywords

  • convolutional neural network
  • gesture recognition
  • EMG
  • signal processing

Cite this

Ke, W., Xing, Y., Di Caterina, G., Petropoulakis, L., & Soraghan, J. (Accepted/In press). Intersected EMG heatmaps and deep learning based gesture recognition. Paper presented at 12th International Conference on Machine Learning and Computing, Shenzhen, China.
Ke, Weijie ; Xing, Yannan ; Di Caterina, Gaetano ; Petropoulakis, Lykourgos ; Soraghan, John. / Intersected EMG heatmaps and deep learning based gesture recognition. Paper presented at 12th International Conference on Machine Learning and Computing, Shenzhen, China.6 p.
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title = "Intersected EMG heatmaps and deep learning based gesture recognition",
abstract = "Hand gesture recognition in myoelectric based prosthetic devices is a key challenge to offering effective solutions to hand/lower arm amputees. A novel hand gesture recognition methodology that employs the difference of EMG energy heatmaps as the input of a specific designed deep learning neural network is presented. Experimental results using data from real amputees indicate that the proposed design achieves 94.31{\%} as average accuracy with best accuracy rate of 98.96{\%}. A comparison of experimental results between the proposed novel hand gesture recognition methodology and other similar approaches indicates the superior effectiveness of the new design.",
keywords = "convolutional neural network, gesture recognition, EMG, signal processing",
author = "Weijie Ke and Yannan Xing and {Di Caterina}, Gaetano and Lykourgos Petropoulakis and John Soraghan",
year = "2019",
month = "10",
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language = "English",
note = "12th International Conference on Machine Learning and Computing, ICMLC 2020 ; Conference date: 15-02-2020 Through 17-02-2020",

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Ke, W, Xing, Y, Di Caterina, G, Petropoulakis, L & Soraghan, J 2020, 'Intersected EMG heatmaps and deep learning based gesture recognition' Paper presented at 12th International Conference on Machine Learning and Computing, Shenzhen, China, 15/02/20 - 17/02/20, .

Intersected EMG heatmaps and deep learning based gesture recognition. / Ke, Weijie; Xing, Yannan; Di Caterina, Gaetano; Petropoulakis, Lykourgos; Soraghan, John.

2020. Paper presented at 12th International Conference on Machine Learning and Computing, Shenzhen, China.

Research output: Contribution to conferencePaper

TY - CONF

T1 - Intersected EMG heatmaps and deep learning based gesture recognition

AU - Ke, Weijie

AU - Xing, Yannan

AU - Di Caterina, Gaetano

AU - Petropoulakis, Lykourgos

AU - Soraghan, John

PY - 2019/10/10

Y1 - 2019/10/10

N2 - Hand gesture recognition in myoelectric based prosthetic devices is a key challenge to offering effective solutions to hand/lower arm amputees. A novel hand gesture recognition methodology that employs the difference of EMG energy heatmaps as the input of a specific designed deep learning neural network is presented. Experimental results using data from real amputees indicate that the proposed design achieves 94.31% as average accuracy with best accuracy rate of 98.96%. A comparison of experimental results between the proposed novel hand gesture recognition methodology and other similar approaches indicates the superior effectiveness of the new design.

AB - Hand gesture recognition in myoelectric based prosthetic devices is a key challenge to offering effective solutions to hand/lower arm amputees. A novel hand gesture recognition methodology that employs the difference of EMG energy heatmaps as the input of a specific designed deep learning neural network is presented. Experimental results using data from real amputees indicate that the proposed design achieves 94.31% as average accuracy with best accuracy rate of 98.96%. A comparison of experimental results between the proposed novel hand gesture recognition methodology and other similar approaches indicates the superior effectiveness of the new design.

KW - convolutional neural network

KW - gesture recognition

KW - EMG

KW - signal processing

M3 - Paper

ER -

Ke W, Xing Y, Di Caterina G, Petropoulakis L, Soraghan J. Intersected EMG heatmaps and deep learning based gesture recognition. 2020. Paper presented at 12th International Conference on Machine Learning and Computing, Shenzhen, China.