Intersected EMG heatmaps and deep learning based gesture recognition

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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
Pages73-78
Number of pages6
DOIs
Publication statusPublished - 17 Feb 2020
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

Keywords

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

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