{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T04:27:41Z","timestamp":1772252861379,"version":"3.50.1"},"reference-count":42,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2019,1,24]],"date-time":"2019-01-24T00:00:00Z","timestamp":1548288000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007161","name":"Secretar\u00eda de Investigaci\u00f3n y Posgrado, Instituto Polit\u00e9cnico Nacional","doi-asserted-by":"publisher","award":["SIP-20180356, SIP-20180637"],"award-info":[{"award-number":["SIP-20180356, SIP-20180637"]}],"id":[{"id":"10.13039\/501100007161","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Classification of electromyographic signals has a wide range of applications, from clinical diagnosis of different muscular diseases to biomedical engineering, where their use as input for the control of prosthetic devices has become a hot topic of research. The challenge of classifying these signals relies on the accuracy of the proposed algorithm and the possibility of its implementation in hardware. This paper considers the problem of electromyography signal classification, solved with the proposed signal processing and feature extraction stages, with the focus lying on the signal model and time domain characteristics for better classification accuracy. The proposal considers a simple preprocessing technique that produces signals suitable for feature extraction and the Burg reflection coefficients to form learning and classification patterns. These coefficients yield a competitive classification rate compared to the time domain features used. Sometimes, the feature extraction from electromyographic signals has shown that the procedure can omit less useful traits for machine learning models. Using feature selection algorithms provides a higher classification performance with as few traits as possible. The algorithms achieved a high classification rate up to 100% with low pattern dimensionality, with other kinds of uncorrelated attributes for hand movement identification.<\/jats:p>","DOI":"10.3390\/s19030475","type":"journal-article","created":{"date-parts":[[2019,1,24]],"date-time":"2019-01-24T11:12:48Z","timestamp":1548328368000},"page":"475","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Hand Movement Classification Using Burg Reflection Coefficients"],"prefix":"10.3390","volume":"19","author":[{"given":"Daniel","family":"Ram\u00edrez-Mart\u00ednez","sequence":"first","affiliation":[{"name":"Centro de Investigaci\u00f3n en Computaci\u00f3n, Instituto Polit\u00e9cnico Nacional, Av. \u201cJuan de Dios B\u00e1tiz\u201d s\/n esq. Miguel Oth\u00f3n de Mendiz\u00e1bal, Col. Nueva Industrial Vallejo, Del. Gustavo A. Madero, Ciudad de M\u00e9xico C.P. 07738, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4270-0350","authenticated-orcid":false,"given":"Mariel","family":"Alfaro-Ponce","sequence":"additional","affiliation":[{"name":"Departamento de Ciencias e Ingenier\u00edas, Universidad Iberoamericana Puebla, Blvrd del Ni\u00f1o Poblano 2901, Reserva Territorial Atlixc\u00e1yotl, Centro Comercial Puebla, San Andr\u00e9s Cholula 72810, Puebla, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1556-8091","authenticated-orcid":false,"given":"Oleksiy","family":"Pogrebnyak","sequence":"additional","affiliation":[{"name":"Centro de Investigaci\u00f3n en Computaci\u00f3n, Instituto Polit\u00e9cnico Nacional, Av. \u201cJuan de Dios B\u00e1tiz\u201d s\/n esq. Miguel Oth\u00f3n de Mendiz\u00e1bal, Col. Nueva Industrial Vallejo, Del. Gustavo A. Madero, Ciudad de M\u00e9xico C.P. 07738, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1504-4714","authenticated-orcid":false,"given":"Mario","family":"Aldape-P\u00e9rez","sequence":"additional","affiliation":[{"name":"Centro de Innovaci\u00f3n y Desarrollo Tecnol\u00f3gico en C\u00f3mputo, Instituto Polit\u00e9cnico Nacional, Av. \u201cJuan de Dios B\u00e1tiz\u201d s\/n esq. Miguel Oth\u00f3n de Mendiz\u00e1bal, Col. Nueva Industrial Vallejo, Del. Gustavo A. Madero, Ciudad de M\u00e9xico C.P. 07700, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8627-4739","authenticated-orcid":false,"given":"Amadeo-Jos\u00e9","family":"Arg\u00fcelles-Cruz","sequence":"additional","affiliation":[{"name":"Centro de Investigaci\u00f3n en Computaci\u00f3n, Instituto Polit\u00e9cnico Nacional, Av. \u201cJuan de Dios B\u00e1tiz\u201d s\/n esq. Miguel Oth\u00f3n de Mendiz\u00e1bal, Col. Nueva Industrial Vallejo, Del. Gustavo A. Madero, Ciudad de M\u00e9xico C.P. 07738, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,1,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"633","DOI":"10.1016\/j.asoc.2015.07.032","article-title":"Nonlinear multiscale Maximal Lyapunov Exponent for accurate myoelectric signal classification","volume":"36","author":"Guo","year":"2015","journal-title":"Appl. Soft Comput. J."},{"key":"ref_2","unstructured":"De Luca, C.J. (2006). Electromyography, John Wiley & Sons, Inc."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1251\/bpo115","article-title":"Techniques of EMG signal analysis: Detection, processing, classification and applications","volume":"8","author":"Reaz","year":"2006","journal-title":"Biol. Proced. Online"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Lahmiri, S., and Boukadoum, M. 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