{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T04:42:27Z","timestamp":1784868147259,"version":"3.55.0"},"reference-count":50,"publisher":"IOP Publishing","issue":"1","license":[{"start":{"date-parts":[[2025,1,17]],"date-time":"2025-01-17T00:00:00Z","timestamp":1737072000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,1,17]],"date-time":"2025-01-17T00:00:00Z","timestamp":1737072000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["J. Neural Eng."],"published-print":{"date-parts":[[2025,2,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>\n                  <jats:italic>Objective.<\/jats:italic> While myoelectric control has been commercialized in prosthetics for decades, its adoption for more general human\u2013machine interaction has been slow. Although high accuracies can be achieved across many gestures, current control approaches are prone to false activations in real-world conditions. This is because the same electromyogram (EMG) signals generated during the elicitation of gestures are also naturally activated when performing activities of daily living (ADLs), such as when driving to work or while typing on a keyboard. This can lead the myoelectric control system, which is trained on a closed set of gestures and thus unaware of the muscle activity associated with these ADLs, to be falsely activated, leading to erroneous inputs and user frustration. <jats:italic>Approach.<\/jats:italic> To overcome this problem, the concept of wake gestures, whereby users could switch between a dedicated control mode and a sleep mode by snapping their fingers, was explored. Using a simple dynamic time warping model, the real-world user-in-the-loop efficacy of wake gestures as a toggle for myoelectric interfaces was demonstrated through two online ubiquitous control tasks with varying levels of difficulty: (1) dismissing an alarm and (2) controlling a robot. <jats:italic>Main results.<\/jats:italic> During these online evaluations, the designed system ignored almost all (<jats:inline-formula>\n                     <jats:tex-math\/>\n                     <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" overflow=\"scroll\">\n                        <mml:mrow>\n                           <mml:mo>&gt;<\/mml:mo>\n                        <\/mml:mrow>\n                     <\/mml:math>\n                  <\/jats:inline-formula>99.9%) non-target EMG activity generated during a set of ADLs (i.e. walking, typing, writing, phone use, and driving), ignored all control gestures (i.e. wrist flexion, wrist extension, hand open, and hand close), and enabled reliable mode switching during intentional wake gesture elicitation. Additionally, questionnaires revealed that participants responded well to the use of wake gestures and generally preferred false negatives over false positives, providing valuable insights into the future design of these systems. <jats:italic>Significance.<\/jats:italic> These results highlight the real-world viability of wake gestures for enabling the intermittent use of myoelectric control, opening up new interaction possibilities for EMG-based inputs.<\/jats:p>","DOI":"10.1088\/1741-2552\/ada4df","type":"journal-article","created":{"date-parts":[[2025,1,2]],"date-time":"2025-01-02T22:53:40Z","timestamp":1735858420000},"page":"016006","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["EMG-based wake gestures eliminate false activations during out-of-set activities of daily living: an online myoelectric control study"],"prefix":"10.1088","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8392-3729","authenticated-orcid":true,"given":"Ethan","family":"Eddy","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5399-4318","authenticated-orcid":false,"given":"Evan","family":"Campbell","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3592-2163","authenticated-orcid":false,"given":"Scott","family":"Bateman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4421-1016","authenticated-orcid":false,"given":"Erik","family":"Scheme","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2025,1,17]]},"reference":[{"key":"jneada4dfbib1","doi-asserted-by":"crossref","DOI":"10.1145\/3544548.3580962","article-title":"A framework and call to action for the future development of EMG-based input in HCI","author":"Eddy","year":"2023"},{"key":"jneada4dfbib2","doi-asserted-by":"publisher","first-page":"503","DOI":"10.1146\/annurev-bioeng-110222-095816","article-title":"Integrating upper-limb prostheses with the human body: technology advances, readiness and roles in human\u2013prosthesis interaction","volume":"26","author":"Huang","year":"2024","journal-title":"Annu. 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