{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T16:55:36Z","timestamp":1783184136494,"version":"3.54.6"},"reference-count":38,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2021,8,23]],"date-time":"2021-08-23T00:00:00Z","timestamp":1629676800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003170","name":"Stiftelsen f\u00f6r Kunskaps- och Kompetensutveckling","doi-asserted-by":"publisher","award":["NA"],"award-info":[{"award-number":["NA"]}],"id":[{"id":"10.13039\/501100003170","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Stiftelsen Promobilia, and the European Commission (FLAG-ERA GRAFIN project)","award":["NA"],"award-info":[{"award-number":["NA"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Pattern recognition algorithms have been widely used to map surface electromyographic signals to target movements as a source for prosthetic control. However, most investigations have been conducted offline by performing the analysis on pre-recorded datasets. While real-time data analysis (i.e., classification when new data becomes available, with limits on latency under 200\u2013300 milliseconds) plays an important role in the control of prosthetics, less knowledge has been gained with respect to real-time performance. Recent literature has underscored the differences between offline classification accuracy, the most common performance metric, and the usability of upper limb prostheses. Therefore, a comparative offline and real-time performance analysis between common algorithms had yet to be performed. In this study, we investigated the offline and real-time performance of nine different classification algorithms, decoding ten individual hand and wrist movements. Surface myoelectric signals were recorded from fifteen able-bodied subjects while performing the ten movements. The offline decoding demonstrated that linear discriminant analysis (LDA) and maximum likelihood estimation (MLE) significantly (p &lt; 0.05) outperformed other classifiers, with an average classification accuracy of above 97%. On the other hand, the real-time investigation revealed that, in addition to the LDA and MLE, multilayer perceptron also outperformed the other algorithms and achieved a classification accuracy and completion rate of above 68% and 69%, respectively.<\/jats:p>","DOI":"10.3390\/s21165677","type":"journal-article","created":{"date-parts":[[2021,8,23]],"date-time":"2021-08-23T23:19:33Z","timestamp":1629760773000},"page":"5677","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Real-Time and Offline Evaluation of Myoelectric Pattern Recognition for the Decoding of Hand Movements"],"prefix":"10.3390","volume":"21","author":[{"given":"Sara","family":"Abbaspour","sequence":"first","affiliation":[{"name":"Department of Neurology, Massachusetts General Hospital and Division of Sleep Medicine, Harvard Medical School, Boston, MA 02114, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8284-5503","authenticated-orcid":false,"given":"Autumn","family":"Naber","sequence":"additional","affiliation":[{"name":"Center for Bionics and Pain Research, 431 80 M\u00f6ndal, Sweden"},{"name":"Department of Electrical Engineering, Chalmers University of Technology, 412 96 Gothenburg, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Max","family":"Ortiz-Catalan","sequence":"additional","affiliation":[{"name":"Center for Bionics and Pain Research, 431 80 M\u00f6ndal, Sweden"},{"name":"Department of Electrical Engineering, Chalmers University of Technology, 412 96 Gothenburg, Sweden"},{"name":"Operational Area 3, Sahlgrenska University Hospital, 431 80 M\u00f6lndal, Sweden"},{"name":"Department of Orthopaedics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, 431 80 M\u00f6lndal, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hamid","family":"GholamHosseini","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronic Engineering, Auckland University of Technology, Auckland 1010, New Zealand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1940-1747","authenticated-orcid":false,"given":"Maria","family":"Lind\u00e9n","sequence":"additional","affiliation":[{"name":"School of Innovation, Design and Engineering, M\u00e4lardalen University, 722 20 V\u00e4ster\u00e5s, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1016\/S1350-4533(99)00066-1","article-title":"Classification of the myoelectric signal using time-frequency based representations","volume":"21","author":"Englehart","year":"1999","journal-title":"Med. 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