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Muscle Tone Assessment by Machine Learning Using Surface Electromyography.
Rezende, Andressa Rastrelo; Alves, Camille Marques; Marques, Isabela Alves; de Souza, Luciane Aparecida Pascucci Sande; Naves, Eduardo Lázaro Martins.
Afiliação
  • Rezende AR; Assistive Technology Laboratory, Faculty of Electrical Engineering, Federal University of Uberlandia, Uberlandia 38400-902, Brazil.
  • Alves CM; Assistive Technology Laboratory, Faculty of Electrical Engineering, Federal University of Uberlandia, Uberlandia 38400-902, Brazil.
  • Marques IA; Assistive Technology Laboratory, Faculty of Electrical Engineering, Federal University of Uberlandia, Uberlandia 38400-902, Brazil.
  • de Souza LAPS; Department of Applied Physical Therapy, Federal University of Triangulo Mineiro, Uberaba 38065-430, Brazil.
  • Naves ELM; Assistive Technology Laboratory, Faculty of Electrical Engineering, Federal University of Uberlandia, Uberlandia 38400-902, Brazil.
Sensors (Basel) ; 24(19)2024 Sep 30.
Article em En | MEDLINE | ID: mdl-39409402
ABSTRACT
Muscle tone is defined as the resistance to passive stretch, but this definition is often criticized for its ambiguity since some suggest it is related to a state of preparation for movement. Muscle tone is primarily regulated by the central nervous system, and individuals with neurological disorders may lose the ability to control normal tone and can exhibit abnormalities. Currently, these abnormalities are mostly evaluated using subjective scales, highlighting a lack of objective assessment methods in the literature. This study aimed to use surface electromyography (sEMG) and machine learning (ML) for the objective classification and characterization of the full spectrum of muscle tone in the upper limb. Data were collected from thirty-nine individuals, including spastic, healthy, hypotonic and rigid subjects. All of the classifiers applied achieved high accuracy, with the best reaching 96.12%, in differentiating muscle tone. These results underscore the potential of the proposed methodology as a more reliable and quantitative method for evaluating muscle tone abnormalities, aiming to address the limitations of traditional subjective assessments. Additionally, the main features impacting the classifiers' performance were identified, which can be utilized in future research and in the development of devices that can be used in clinical practice.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Eletromiografia / Aprendizado de Máquina / Tono Muscular Limite: Adult / Female / Humans / Male Idioma: En Revista: Sensors (Basel) Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Brasil País de publicação: Suíça

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Eletromiografia / Aprendizado de Máquina / Tono Muscular Limite: Adult / Female / Humans / Male Idioma: En Revista: Sensors (Basel) Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Brasil País de publicação: Suíça