Improved High-density Myoelectric Pattern Recognition Control Against Electrode Shift Using Data Augmentation and Dilated Convolutional Neural Network

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, 2020.

Cited by: 0|Bibtex|Views14|DOI:https://doi.org/10.1109/TNSRE.2020.3030931
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Other Links: pubmed.ncbi.nlm.nih.gov|academic.microsoft.com

Abstract:

This work demonstrated feasibility and usability of combining data augmentation and DCNN in predict-ing myoelectric patterns in the context of electrode shifts. Signifi-cance: The proposed method is a practical solution for robust my-oelectric control against electrode array shifts.

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