A Deep Transfer Learning Approach to Reducing the Effect of Electrode Shift in EMG Pattern Recognition-Based Control

Simple item page
dc.contributor.authorAmeri, Ali
dc.contributor.authorAkhaee, Mohammad Ali
dc.contributor.authorScheme, Erik
dc.contributor.authorEnglehart, Kevin
dc.date.accessioned2023-06-19T17:58:34Z
dc.date.available2023-06-19T17:58:34Z
dc.date.issued2020
dc.description.abstractAn important barrier to commercialization of pattern recognition myoelectric control of prostheses is the lack of robustness to confounding factors such as electrode shift, skin impedance variations, and learning effects. To overcome this challenge, a novel supervised adaptation approach based on transfer learning (TL) with convolutional neural networks (CNNs) is proposed which requires only a short training session (a few seconds for each class) to recalibrate the system. TL is proposed as a solution to the problem of insufficient calibration data due to short training times for both classification and regression-based control schemes. This approach was validated for electrode shift of roughly 2.5cm with 13 able-bodied subjects to estimate individual and combined wrist motions. With this method, the original CNN (trained before the shift) was fine-tuned with the calibration data from after shifting. The results show that the proposed technique outperforms training a CNN from scratch (random initialization of weights) or a support vector machine (SVM) using the minimal calibration data. Moreover, it demonstrates superior performance than previous LDA and QDA-based adaptation approaches. As the outcomes confirm, the proposed CNN TL method provides a practical solution for adaptation to external factors, improving the robustness of electromyogram (EMG) pattern recognition systems.
dc.description.copyright© 2019 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.
dc.identifier.doi10.1109/tnsre.2019.2962189
dc.identifier.issn1534-4320
dc.identifier.issn1558-0210
dc.identifier.urihttps://unbscholar.lib.unb.ca/handle/1882/37231
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers
dc.relationNSERC
dc.relation.hasversionhttps://doi.org/10.1109/tnsre.2019.2962189
dc.relation.ispartofIEEE Transactions on Neural Systems and Rehabilitation Engineering
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.subject.disciplineElectrical and Computer Engineering
dc.titleA Deep Transfer Learning Approach to Reducing the Effect of Electrode Shift in EMG Pattern Recognition-Based Control
dc.typejournal-article
oaire.citation.endPage379
oaire.citation.issue2
oaire.citation.startPage370
oaire.citation.titleIEEE Transactions on Neural Systems and Rehabilitation Engineering
oaire.citation.volume28
oaire.license.conditionother
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
A_Deep_Transfer_Learning_Approach_to_Reducing_the_Effect_of_Electrode_Shift_in_EMG_Pattern_Recognition-Based_Control.pdf
Size:
1.62 MB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.13 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Journal Articles