Knee Functional State Classification Using Surface Electromyographic and Goniometric Signals by Means of Artificial Neural Networks
In this article a methodology for a medical diagnostic decision support system to assess knee injuries is proposed. Such methodology takes into account that these types of injuries are common and arise due to different causes. Therefore, the physician's diagnosis and treatment may lead to expen...
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| Veröffentlicht in: | Ingeniería y universidad 2015, Vol.19 (1), p.51-66 |
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| creator | Herrera-González, Marcelo Martínez-Hernández, Gustavo Adolfo Rodríguez-Sotelo, José Luis Avilés-Sánchez, Óscar Fernando |
| description | In this article a methodology for a medical diagnostic decision support system to assess knee injuries is proposed. Such methodology takes into account that these types of injuries are common and arise due to different causes. Therefore, the physician's diagnosis and treatment may lead to expensive and invasive tests depending on his medical criteria. This system uses a surface Electromyographic (sEMG) and goniometric signals that are processed with signal analysis methods in time-frequency space through a spectrogram and a wavelet transform. Artificial neural networks are used as a learning technique by having a multilayer perceptron. EMG signals were measured in four external and internal muscles associated to the joint through flexion and extension assessments. These tests also registered the goniometric measures of the sagittal plane. This system shows above 80% of effectiveness as a performance measure that makes it an objective measure leading to help the physician in his diagnosis. |
| doi_str_mv | 10.11144/Javeriana.iyu19-1.kfsc |
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Such methodology takes into account that these types of injuries are common and arise due to different causes. Therefore, the physician's diagnosis and treatment may lead to expensive and invasive tests depending on his medical criteria. This system uses a surface Electromyographic (sEMG) and goniometric signals that are processed with signal analysis methods in time-frequency space through a spectrogram and a wavelet transform. Artificial neural networks are used as a learning technique by having a multilayer perceptron. EMG signals were measured in four external and internal muscles associated to the joint through flexion and extension assessments. These tests also registered the goniometric measures of the sagittal plane. This system shows above 80% of effectiveness as a performance measure that makes it an objective measure leading to help the physician in his diagnosis.</description><identifier>ISSN: 0123-2126</identifier><identifier>EISSN: 2011-2769</identifier><identifier>DOI: 10.11144/Javeriana.iyu19-1.kfsc</identifier><language>eng</language><publisher>Bogotá: Editorial Pontificia Universidad Javeriana</publisher><subject>ANN ; Archives & records ; Artificial intelligence ; Artificial neural networks ; Biomechanics ; Biomedical engineering ; Classification ; Diagnosis ; Diagnostic systems ; Electromyography ; EMGS ; ENGINEERING, MULTIDISCIPLINARY ; goniometry ; goniometría ; Injuries ; Kinematics ; Kinesiology ; Knee ; knee injury ; lesión de rodilla ; Multilayer perceptrons ; Muscle function ; Muscles ; Pain ; Rehabilitation ; RNA ; sEMG ; Signal analysis ; Signal processing ; Sports injuries ; Studies ; Support systems ; transformada wavelet ; Wavelet analysis ; Wavelet Transform ; Wavelet transforms</subject><ispartof>Ingeniería y universidad, 2015, Vol.19 (1), p.51-66</ispartof><rights>2015. This work is licensed under (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>This work is licensed under a Creative Commons Attribution 4.0 International License.</rights><rights>LICENCIA DE USO: Los documentos a texto completo incluidos en Dialnet son de acceso libre y propiedad de sus autores y/o editores. Por tanto, cualquier acto de reproducción, distribución, comunicación pública y/o transformación total o parcial requiere el consentimiento expreso y escrito de aquéllos. Cualquier enlace al texto completo de estos documentos deberá hacerse a través de la URL oficial de éstos en Dialnet. Más información: https://dialnet.unirioja.es/info/derechosOAI | INTELLECTUAL PROPERTY RIGHTS STATEMENT: Full text documents hosted by Dialnet are protected by copyright and/or related rights. This digital object is accessible without charge, but its use is subject to the licensing conditions set by its authors or editors. Unless expressly stated otherwise in the licensing conditions, you are free to linking, browsing, printing and making a copy for your own personal purposes. All other acts of reproduction and communication to the public are subject to the licensing conditions expressed by editors and authors and require consent from them. Any link to this document should be made using its official URL in Dialnet. More info: https://dialnet.unirioja.es/info/derechosOAI</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,314,776,780,870,881,4010,27900,27901,27902</link.rule.ids></links><search><creatorcontrib>Herrera-González, Marcelo</creatorcontrib><creatorcontrib>Martínez-Hernández, Gustavo Adolfo</creatorcontrib><creatorcontrib>Rodríguez-Sotelo, José Luis</creatorcontrib><creatorcontrib>Avilés-Sánchez, Óscar Fernando</creatorcontrib><title>Knee Functional State Classification Using Surface Electromyographic and Goniometric Signals by Means of Artificial Neural Networks</title><title>Ingeniería y universidad</title><addtitle>Ing. Univ</addtitle><description>In this article a methodology for a medical diagnostic decision support system to assess knee injuries is proposed. Such methodology takes into account that these types of injuries are common and arise due to different causes. Therefore, the physician's diagnosis and treatment may lead to expensive and invasive tests depending on his medical criteria. This system uses a surface Electromyographic (sEMG) and goniometric signals that are processed with signal analysis methods in time-frequency space through a spectrogram and a wavelet transform. Artificial neural networks are used as a learning technique by having a multilayer perceptron. EMG signals were measured in four external and internal muscles associated to the joint through flexion and extension assessments. These tests also registered the goniometric measures of the sagittal plane. 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| subjects | ANN Archives & records Artificial intelligence Artificial neural networks Biomechanics Biomedical engineering Classification Diagnosis Diagnostic systems Electromyography EMGS ENGINEERING, MULTIDISCIPLINARY goniometry goniometría Injuries Kinematics Kinesiology Knee knee injury lesión de rodilla Multilayer perceptrons Muscle function Muscles Pain Rehabilitation RNA sEMG Signal analysis Signal processing Sports injuries Studies Support systems transformada wavelet Wavelet analysis Wavelet Transform Wavelet transforms |
| title | Knee Functional State Classification Using Surface Electromyographic and Goniometric Signals by Means of Artificial Neural Networks |
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