Multiday Evaluation of Techniques for EMG-Based Classification of Hand Motions

Simple item page
dc.contributor.authorWaris, Asim
dc.contributor.authorNiazi, Imran K.
dc.contributor.authorJamil, Mohsin
dc.contributor.authorEnglehart, Kevin
dc.contributor.authorJensen, Winnie
dc.contributor.authorKamavuako, Ernest Nlandu
dc.date.accessioned2023-06-30T16:52:56Z
dc.date.available2023-06-30T16:52:56Z
dc.date.issued2019-07
dc.description.abstractCurrently, most of the adopted myoelectric schemes for upper limb prostheses do not provide users with intuitive control. Higher accuracies have been reported using different classification algorithms but investigation on the reliability over time for these methods is very limited. In this study, we compared for the first time the longitudinal performance of selected state-of-the-art techniques for electromyography (EMG) based classification of hand motions. Experiments were conducted on ten able-bodied and six transradial amputees for seven continuous days. Linear discriminant analysis (LDA), artificial neural network (ANN), support vector machine (SVM), K-nearest neighbor (KNN), and decision trees (TREE) were compared. Comparative analysis showed that the ANN attained highest classification accuracy followed by LDA. Three-way repeated ANOVA test showed a significant difference (P <; 0.001) between EMG types (surface, intramuscular, and combined), days (1-7), classifiers, and their interactions. Performance on the last day was significantly better (P <; 0.05) than the first day for all classifiers and EMG types. Within-day, classification error (WCE) across all subject and days in ANN was: surface (9.12 ± 7.38%), intramuscular (11.86 ± 7.84%), and combined (6.11 ± 7.46%). The between-day analysis in a leave-one-day-out fashion showed that the ANN was the optimal classifier surface (21.88 ± 4.14%), intramuscular (29.33 ± 2.58%), and combined (14.37 ± 3.10%). Results indicate that within day performances of classifiers may be similar but over time, it may lead to a substantially different outcome. Furthermore, training ANN on multiple days might allow capturing time-dependent variability in the EMG signals and thus minimizing the necessity for daily system recalibration.
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/jbhi.2018.2864335
dc.identifier.issn2168-2194
dc.identifier.issn2168-2208
dc.identifier.urihttps://unbscholar.lib.unb.ca/handle/1882/37246
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers
dc.relationHigher Education Commission of Pakistan
dc.relationNational University of Sciences and Technology
dc.relation.hasversionhttps://doi.org/10.1109/jbhi.2018.2864335
dc.relation.ispartofIEEE Journal of Biomedical and Health Informatics
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.subject.disciplineElectrical and Computer Engineering
dc.titleMultiday Evaluation of Techniques for EMG-Based Classification of Hand Motions
dc.typejournal-article
oaire.citation.endPage1534
oaire.citation.issue4
oaire.citation.startPage1526
oaire.citation.titleIEEE Journal of Biomedical and Health Informatics
oaire.citation.volume23
oaire.license.conditionother
oaire.versionhttp://purl.org/coar/version/c_ab4af688f83e57aa

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Multiday_Evaluation_of_Techniques_for_EMG_Based_Classification_of_Hand_Motions[18].pdf
Size:
906.55 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