A Novel Duo-Stage driven Deep Neural Network Approach for Mitigating Electrode Shift Impact on Myoelectric Pattern Recognition Systems

Conference paper


Kulwa, F., Samuel, O.W., Asogbon, M., Oyemakinde, T.T., Obe, O.O. and Li, G. 2023. A Novel Duo-Stage driven Deep Neural Network Approach for Mitigating Electrode Shift Impact on Myoelectric Pattern Recognition Systems. 2023 IEEE International Symposium on Medical Measurements and Applications (MeMeA). IEEE. https://doi.org/10.1109/MeMeA57477.2023.10171910
AuthorsKulwa, F., Samuel, O.W., Asogbon, M., Oyemakinde, T.T., Obe, O.O. and Li, G.
TypeConference paper
Abstract

A major barrier to the commercialization of pattern recognition (PR)-based myoelectric prostheses is the lack of robustness to confounding factors such as electrode shift which has been lingering for years. To overcome this challenge, a novel Duo-Stage Convolutional Neural Network (DS-CNN) is proposed. The DS-CNN is comprised of two cascaded stages in which the first stage deciphers the occurrence of a particular kind of shift upon which a requisite CNN model is triggered in the second stage for accurate decoding of individual motion intent, which is necessary for initiating robust control of the prostheses. The proposed scheme works on raw EMG signals as input which reduces the preprocessing time that would be required in conventional machine learning-based PR schemes, to effectively mitigate both transverse and longitudinal shifts using the same network architecture. This approach was validated for four distinct electrode shift conditions (with shifts in the range of 7.50mm-10.05mm) in a dataset obtained from 18 able-bodied subjects that performed 8 classes of targeted hand gestures. The experimental results show that the proposed dual-stage driven deep neural network model can adequately resolve the effects of electrode shift with classification accuracy near the No-shift scenario (< 1.70% difference between shift mitigation and No shift scenarios). These outcomes suggest that our method can provide a practical solution for adaptation to electrode shift, thus improving the robustness of the EMG pattern recognition systems in both clinical and commercial settings.

KeywordsPattern Recognition; Upper Limb Prostheses; Electromyogram (EMG); Electrode Shift
Year2023
Conference2023 IEEE International Symposium on Medical Measurements and Applications (MeMeA)
PublisherIEEE
Digital Object Identifier (DOI)https://doi.org/10.1109/MeMeA57477.2023.10171910
Web address (URL)https://ieeexplore.ieee.org/abstract/document/10171910
Publisher's version
License
All rights reserved
File Access Level
Restricted
Journal citationpp. 1-6
ISBN9781665493840
Web address (URL) of conference proceedingshttps://memea2023.ieee-ims.org/
File
File Access Level
Restricted
Output statusPublished
Publication dates
Online10 Jul 2023
Publication process dates
Accepted20 Mar 2023
Deposited03 Oct 2023
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https://repository.derby.ac.uk/item/q1vzy/a-novel-duo-stage-driven-deep-neural-network-approach-for-mitigating-electrode-shift-impact-on-myoelectric-pattern-recognition-systems

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