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Neural Eng."],"published-print":{"date-parts":[[2025,10,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>\n                  <jats:italic>Objective.<\/jats:italic> Wrist electromyography (EMG) is emerging as an enticing wearable input modality for human\u2013machine interaction. Traditionally recorded from the forearm for use in transradial prostheses, wrist-based EMG sensors are now being integrated into devices such as watches and wristbands for hand gesture recognition (HGR). Consumer familiarity with wrist-worn devices makes wrist EMG a compelling option, but the need for individualized user calibration remains a challenge. <jats:italic>Approach.<\/jats:italic> This study therefore evaluated various cross-user models to reduce the calibration burden and compared wrist- and forearm-based models. Eight different machine learning architectures were evaluated across 33 users, using varying amounts of data from the end user. <jats:italic>Main results.<\/jats:italic> A temporal convolutional network-bidirectional long short-term memory architecture, applied for the first time to EMG classification, was found to significantly (<jats:italic>p<\/jats:italic>\u2009&lt;\u20090.05) outperform other tested machine learning architectures. An inter-day feature set combined with <jats:italic>Z<\/jats:italic>-score normalization achieved the best performance when classifying five gestures (plus a rest class) using either wrist or forearm EMG. Consistent with other recent results, wrist EMG consistently outperformed forearm EMG in all analyses, including within- and across-user comparisons (<jats:italic>p<\/jats:italic>\u2009&lt;\u20090.05). In cross-user models, wrist EMG demonstrated a zero-shot performance of 78.2%, compared to 71.6% for forearm EMG (<jats:italic>p<\/jats:italic>\u2009&lt;\u20090.05). Introducing one calibration repetition from the end user increased one-shot performance of wrist EMG to 91.6%, compared to 86.9% for forearm EMG (<jats:italic>p<\/jats:italic>\u2009&lt;\u20090.05). Adding further training repetitions boosted wrist EMG performance to 98.3%, compared to 97.4% for forearm EMG. <jats:italic>Significance.<\/jats:italic> These findings provide new evidence supporting the viability of wrist EMG for cross-user HGR models that generalize to new users with minimal calibration, suggesting promising potential for its broader adoption in wearable devices.<\/jats:p>","DOI":"10.1088\/1741-2552\/ae08eb","type":"journal-article","created":{"date-parts":[[2025,9,18]],"date-time":"2025-09-18T22:51:59Z","timestamp":1758235919000},"page":"056018","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["From zero- to few-shot: deep temporal learning of wrist EMG enables scalable cross-user gesture recognition"],"prefix":"10.1088","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9311-1345","authenticated-orcid":true,"given":"Fady S","family":"Botros","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8051-3751","authenticated-orcid":true,"given":"Heather E","family":"Williams","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0170-3245","authenticated-orcid":false,"given":"Angkoon","family":"Phinyomark","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4421-1016","authenticated-orcid":false,"given":"Erik J","family":"Scheme","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2025,9,26]]},"reference":[{"key":"jneae08ebbib1","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1109\/RBME.2021.3078190","type":"journal-article","article-title":"Emerging wearable interfaces and algorithms for hand gesture recognition: a survey","volume":"15","author":"Jiang","year":"2021","journal-title":"IEEE Rev. 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