{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T21:50:38Z","timestamp":1790113838526,"version":"4.0.1"},"reference-count":91,"publisher":"Public Library of Science (PLoS)","issue":"5","license":[{"start":{"date-parts":[[2024,5,13]],"date-time":"2024-05-13T00:00:00Z","timestamp":1715558400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100017777","name":"Philanthropic Educational Organization","doi-asserted-by":"publisher","award":["PEO Scholar Award"],"award-info":[{"award-number":["PEO Scholar Award"]}],"id":[{"id":"10.13039\/100017777","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004073","name":"Killam Trusts","doi-asserted-by":"publisher","award":["Izaak Walton Killam Memorial Scholarship"],"award-info":[{"award-number":["Izaak Walton Killam Memorial Scholarship"]}],"id":[{"id":"10.13039\/501100004073","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science and Engineering Research Council","award":["CGS-D"],"award-info":[{"award-number":["CGS-D"]}]},{"name":"Natural Science and Engineering Research Council","award":["DG RGPIN-2019-05961"],"award-info":[{"award-number":["DG RGPIN-2019-05961"]}]},{"name":"Natural Science and Engineering Research Council","award":["DG RGPIN-2015-03646"],"award-info":[{"award-number":["DG RGPIN-2015-03646"]}]},{"name":"The Sensory Motor Adaptive Rehabilitation Technology Network at the University of Alberta"},{"DOI":"10.13039\/100013373","name":"Alberta Machine Intelligence Institute","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100013373","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Canada CIFAR AI Chairs program"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:p>Upper limb robotic (myoelectric) prostheses are technologically advanced, but challenging to use. In response, substantial research is being done to develop person-specific prosthesis controllers that can predict a user\u2019s intended movements. Most studies that test and compare new controllers rely on simple assessment measures such as task scores (e.g., number of objects moved across a barrier) or duration-based measures (e.g., overall task completion time). These assessment measures, however, fail to capture valuable details about: the quality of device arm movements; whether these movements match users\u2019 intentions; the timing of specific wrist and hand control functions; and users\u2019 opinions regarding overall device reliability and controller training requirements. In this work, we present a comprehensive and novel suite of myoelectric prosthesis control evaluation metrics that better facilitates analysis of device movement details\u2014spanning measures of task performance, control characteristics, and user experience. As a case example of their use and research viability, we applied these metrics in real-time control experimentation. Here, eight participants without upper limb impairment compared device control offered by a deep learning-based controller (recurrent convolutional neural network-based classification with transfer learning, or RCNN-TL) to that of a commonly used controller (linear discriminant analysis, or LDA). The participants wore a simulated prosthesis and performed complex functional tasks across multiple limb positions. Analysis resulting from our suite of metrics identified 16 instances of a user-facing problem known as the \u201climb position effect\u201d. We determined that RCNN-TL performed the same as or significantly better than LDA in four such problem instances. We also confirmed that transfer learning can minimize user training burden. Overall, this study contributes a multifaceted new suite of control evaluation metrics, along with a guide to their application, for use in research and testing of myoelectric controllers today, and potentially for use in broader rehabilitation technologies of the future.<\/jats:p>","DOI":"10.1371\/journal.pone.0291279","type":"journal-article","created":{"date-parts":[[2024,5,13]],"date-time":"2024-05-13T13:21:59Z","timestamp":1715606519000},"page":"e0291279","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":7,"title":["A multifaceted suite of metrics for comparative myoelectric prosthesis controller research"],"prefix":"10.1371","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8051-3751","authenticated-orcid":true,"given":"Heather E.","family":"Williams","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8442-9901","authenticated-orcid":true,"given":"Ahmed W.","family":"Shehata","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kodi Y.","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0788-0568","authenticated-orcid":true,"given":"Jacqueline S.","family":"Hebert","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1686-2978","authenticated-orcid":true,"given":"Patrick M.","family":"Pilarski","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2024,5,13]]},"reference":[{"key":"pone.0291279.ref001","doi-asserted-by":"crossref","unstructured":"Braza DW, Yacub Martin JN. 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