On the robustness of real-time myoelectric control investigations: a multiday Fitts’ law approach

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dc.contributor.authorWaris, Asim
dc.contributor.authorMendez, Irene
dc.contributor.authorEnglehart, Kevin
dc.contributor.authorJensen, Winnie
dc.contributor.authorKamavuako, Ernest Nlandu
dc.date.accessioned2023-06-30T12:51:35Z
dc.date.available2023-06-30T12:51:35Z
dc.date.issued2019
dc.description.abstractObjective. Real-time myoelectric experimental protocol is considered as a means to quantify usability of myoelectric control schemes. While usability should be considered over time to assure clinical robustness, all real-time studies reported thus far are limited to a single session or day and thus the influence of time on real-time performance is still unexplored. In this study, the aim was to develop a novel experimental protocol to quantify the effect of time on real-time performance measures over multiple days using a Fitts' law approach. Approach. Four metrics: throughput, completion rate, path efficiency and overshoot, were assessed using three train-test strategies: (i) an artificial neural network (ANN) classifier was trained on data collected from the previous day and tested on present day (BDT) (ii) trained and tested on the same day (WDT) and (iii) trained on all previous days including present day and tested on present day (CDT) in a week-long experimental protocol. Main results. It was found that on average, the completion rate (98.37%  ±  1.47%) of CDT was significantly better (P  <  0.01) than that of BDT (86.25%  ±  3.46%) and WDT (94.22%  ±  2.74%). The throughput (0.40  ±  0.03 bits s−1) of CDT was significantly better (P  =  0.001) than that of BDT (0.38  ±  0.03 bits s−1). Offline analysis showed a different trend due to the difference in the training strategies. Significance. Results suggest that increasing the size of the training set over time can be beneficial to assure robust performance of the system over time.
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/aae9d4
dc.identifier.doi10.1088/1741-2552/aae9d4
dc.identifier.issn1741-2560
dc.identifier.issn1741-2552
dc.identifier.urihttps://unbscholar.lib.unb.ca/handle/1882/37240
dc.language.isoen
dc.publisherIOP Publishing
dc.relationHigher Education Commission of Pakistan
dc.relation.hasversionhttps://doi.org/10.1088/1741-2552/aae9d4
dc.relation.ispartofJournal of Neural Engineering
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.subject.disciplineElectrical and Computer Engineering
dc.titleOn the robustness of real-time myoelectric control investigations: a multiday Fitts’ law approach
dc.typejournal-article
oaire.citation.issue2
oaire.citation.titleJournal of Neural Engineering
oaire.citation.volume16
oaire.license.conditionother
oaire.versionhttp://purl.org/coar/version/c_ab4af688f83e57aa

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