A proportional control scheme for high density force myography

Simple item page
dc.contributor.authorBelyea, Alexander T.
dc.contributor.authorEnglehart, Kevin B.
dc.contributor.authorScheme, Erik J.
dc.date.accessioned2023-06-30T13:14:51Z
dc.date.available2023-06-30T13:14:51Z
dc.date.issued2018-08
dc.description.abstractObjective. Force myography (FMG) has been shown to be a potentially higher accuracy alternative to electromyography for pattern recognition based prosthetic control. Classification accuracy, however, is just one factor that affects the usability of a control system. Others, like the ability to start and stop, to coordinate dynamic movements, and to control the velocity of the device through some proportional control scheme can be of equal importance. To impart effective fine control using FMG-based pattern recognition, it is important that a method of controlling the velocity of each motion be developed. Methods. In this work force myography data were collected from 14 able bodied participants and one amputee participant as they performed a set of wrist and hand motions. The offline proportional control performance of a standard mean signal amplitude approach and a proposed regression-based alternative was compared. The impact of providing feedback during training, as well as the use of constrained or unconstrained hand and wrist contractions, were also evaluated. Results. It is shown that the commonly used mean of rectified channel amplitudes approach commonly employed with electromyography does not translate to force myography. The proposed class-based regression proportional control approach is shown significantly outperform this standard approach (ρ  <  0.001), yielding a R2 correlation coefficients of 0.837 and 0.830 for constrained and unconstrained forearm contractions, respectively for able bodied participants. No significant difference (ρ  =  0.693) was found in R2 performance when feedback was provided during training or not. The amputee subject achieved a classification accuracy of 83.4%  ±  3.47% demonstrating the ability to distinguish contractions well with FMG. In proportional control the amputee participant achieved an R2 of of 0.375 for regression based proportional control during unconstrained contractions. This is lower than the unconstrained case for able-bodied subjects for this particular amputee, possibly due to difficultly in visualizing contraction level modulation without feedback. This may be remedied in the use of a prosthetic limb that would provide real-time feedback in the form of device speed. Conclusion. A novel class-specific regression-based approach is proposed for multi-class control is described and shown to provide an effective means of providing FMG-based proportional control.
dc.description.copyrightThis is an author-created, un-copyedited version of an article accepted for publication/published in Journal of Neural Engineering. IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record is available online at https://doi.org/10.1088/1741-2552/aac89b
dc.identifier.doi10.1088/1741-2552/aac89b
dc.identifier.issn1741-2560
dc.identifier.issn1741-2552
dc.identifier.urihttps://unbscholar.lib.unb.ca/handle/1882/37242
dc.language.isoen
dc.publisherIOP Publishing
dc.relationNSERC
dc.relation.hasversionhttps://doi.org/10.1088/1741-2552/aac89b
dc.relation.ispartofJournal of Neural Engineering
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.subject.disciplineElectrical and Computer Engineering
dc.titleA proportional control scheme for high density force myography
dc.typejournal-article
oaire.citation.issue4
oaire.citation.titleJournal of Neural Engineering
oaire.citation.volume15
oaire.license.conditionother
oaire.versionhttp://purl.org/coar/version/c_ab4af688f83e57aa

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
A Proportional Control Scheme for High Density Force Myography,” Journal of Neural Engineering.pdf
Size:
779.02 KB
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