Multiday Evaluation of Techniques for EMG-Based Classification of Hand Motions
| dc.contributor.author | Waris, Asim | |
| dc.contributor.author | Niazi, Imran K. | |
| dc.contributor.author | Jamil, Mohsin | |
| dc.contributor.author | Englehart, Kevin | |
| dc.contributor.author | Jensen, Winnie | |
| dc.contributor.author | Kamavuako, Ernest Nlandu | |
| dc.date.accessioned | 2023-06-30T13:04:34Z | |
| dc.date.available | 2023-06-30T13:04:34Z | |
| dc.date.issued | 2019-07 | |
| dc.description.abstract | Currently, 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.doi | 10.1109/jbhi.2018.2864335 | |
| dc.identifier.issn | 2168-2194 | |
| dc.identifier.issn | 2168-2208 | |
| dc.identifier.uri | https://unbscholar.lib.unb.ca/handle/1882/37241 | |
| dc.language.iso | en | |
| dc.publisher | Institute of Electrical and Electronics Engineers | |
| dc.relation | Higher Education Commission of Pakistan | |
| dc.relation | National University of Sciences and Technology | |
| dc.relation.hasversion | https://doi.org/10.1109/jbhi.2018.2864335 | |
| dc.relation.ispartof | IEEE Journal of Biomedical and Health Informatics | |
| dc.rights | http://purl.org/coar/access_right/c_abf2 | |
| dc.subject.discipline | Electrical and Computer Engineering | |
| dc.title | Multiday Evaluation of Techniques for EMG-Based Classification of Hand Motions | |
| dc.type | journal-article | |
| oaire.citation.endPage | 1534 | |
| oaire.citation.issue | 4 | |
| oaire.citation.startPage | 1526 | |
| oaire.citation.title | IEEE Journal of Biomedical and Health Informatics | |
| oaire.citation.volume | 23 | |
| oaire.license.condition | other | |
| oaire.version | http://purl.org/coar/version/c_ab4af688f83e57aa |