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A ternary gesture classification problem is presented by states of <jats:italic>\u2019thumbs up\u2019<\/jats:italic>, <jats:italic>\u2019thumbs down\u2019<\/jats:italic>, and <jats:italic>\u2019relax\u2019<\/jats:italic> in order to communicate in the affirmative or negative in a non-verbal fashion to a machine. Of the nine statistical learning paradigms benchmarked over 10-fold cross validation (with three methods of feature selection), an ensemble of Random Forest and Support Vector Machine through voting achieves the best score of 91.74% with a rule-based feature selection method. When new subjects are considered, this machine learning approach fails to generalise new data, and thus the processes of Inductive and Supervised Transductive Transfer Learning are introduced with a short calibration exercise (15 s). Failure of generalisation shows that 5 s of data per-class is the strongest for classification (versus one through seven seconds) with only an accuracy of 55%, but when a short 5 s per class calibration task is introduced via the suggested transfer method, a Random Forest can then classify unseen data from the calibrated subject at an accuracy of around 97%, outperforming the 83% accuracy boasted by the proprietary Myo system. Finally, a preliminary application is presented through social interaction with a humanoid Pepper robot, where the use of our approach and a most-common-class metaclassifier achieves 100% accuracy for all trials of a \u201820 Questions\u2019 game.<\/jats:p>","DOI":"10.1007\/s12652-020-01852-z","type":"journal-article","created":{"date-parts":[[2020,3,7]],"date-time":"2020-03-07T06:02:58Z","timestamp":1583560978000},"page":"6021-6031","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["Thumbs up, thumbs down: non-verbal human-robot interaction through real-time EMG classification via inductive and supervised transductive transfer learning"],"prefix":"10.1007","volume":"11","author":[{"given":"Jhonatan","family":"Kobylarz","sequence":"first","affiliation":[]},{"given":"Jordan J.","family":"Bird","sequence":"additional","affiliation":[]},{"given":"Diego R.","family":"Faria","sequence":"additional","affiliation":[]},{"given":"Eduardo Parente","family":"Ribeiro","sequence":"additional","affiliation":[]},{"given":"Anik\u00f3","family":"Ek\u00e1rt","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,3,7]]},"reference":[{"key":"1852_CR1","unstructured":"Abduo M, Galster M (2015) Myo gesture control armband for medical applications. https:\/\/www.semanticscholar.org\/paper\/Myo-Gesture-Control-Armband-for-Medical-Abduo-Galster\/3b5ed355b09beecb7b2b6bbd23fead44b50374c6"},{"key":"1852_CR2","doi-asserted-by":"crossref","unstructured":"Abreu JG, Teixeira JM, Figueiredo LS, Teichrieb V (2016) Evaluating sign language recognition using the myo armband. 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