Computational Feedback Tool for Muscular Rehabilitation Based in Quantitative Analysis of sEMG Signals

Carlos Quizhpe-Cárdenas; Francisco Ortiz-Ortiz; Freddy Bueno-Palomeque; Marco Vinicio Vásquez Cabrera

doi:10.1007/978-3-319-94484-5_10

Computational Feedback Tool for Muscular Rehabilitation Based in Quantitative Analysis of sEMG Signals

Carlos Quizhpe-Cárdenas
, Francisco Ortiz-Ortiz
, Freddy Bueno-Palomeque
, Marco Vinicio Vásquez Cabrera

Grupo de Investigación en Ingeniería Biomédica (GIIB)

Producción científica: Capítulo del libro/informe/acta de congreso › Capítulo

1 Cita (Scopus)

Resumen

Processing sEMG signals in muscle rehabilitation has permitted to measure, register, and use different quantification methods as a biofeedback tool of the techniques used in this area. This study presents a computational tool based in the Wavelet Transform to filter and acquire only the most relevant frequency bands of sEMG signals. Time and frequency analysis were also included. To determine the signal variation of a patient, a comparative analysis can be performed from the beginning of the therapy to a selected date; furthermore, it is possible to compare the behavior and differences among patients. The program was tested by physiotherapists of the IPCA, with sEMG signals of patients with spastic CP. The results delivered by the application agreed with the results of the medical diagnoses, becoming a tool that allows to make decisions about the applied therapies, either to make changes, or to quantify the benefit of this on patients.

Idioma original	Inglés
Título de la publicación alojada	Computational Feedback Tool for Muscular Rehabilitation Based in Quantitative Analysis of sEMG Signals
Editores	Waldemar Karwowski, Ravindra S. Goonetilleke
Páginas	94-101
Número de páginas	8
ISBN (versión digital)	9783319944838
DOI	https://doi.org/10.1007/978-3-319-94484-5_10
Estado	Publicada - 1 ene. 2019
Evento	Advances in Intelligent Systems and Computing - , Alemania Duración: 1 ene. 2015 → …

Serie de la publicación

Nombre	Advances in Intelligent Systems and Computing
Volumen	789
ISSN (versión impresa)	2194-5357

Conferencia

Conferencia	Advances in Intelligent Systems and Computing
País/Territorio	Alemania
Período	1/01/15 → …

Areas de Conocimiento del CACES

419A Tecnología de diagnóstico y tratamiento médico

Acceder al documento

10.1007/978-3-319-94484-5_10

Otros archivos y enlaces

Valoración del movimiento de extensión en el área cervical mediante el análisis de señales electromiográficas en pacientes con PCI espástico
Bueno Palomeque, F. L. (Investigador principal), Ortiz Ortiz, F. J. (Estudiante Investigador), Quizhpe Cardenas, C. E. (Estudiante Investigador), López Delis, A. (Investigador Externo), Fajardo Taday, F. F. (Estudiante Investigador), Avila Lapo, J. M. (Estudiante Investigador), Lopez Martinez, P. E. (Estudiante Investigador) & Merchan Carrillo, D. F. (Estudiante Investigador)
15/12/16 → 30/09/22
Proyecto: Investigación y Desarrollo

Citar esto

Quizhpe-Cárdenas, C., Ortiz-Ortiz, F., Bueno-Palomeque, F., & Cabrera, M. V. V. (2019). Computational Feedback Tool for Muscular Rehabilitation Based in Quantitative Analysis of sEMG Signals. En W. Karwowski, & R. S. Goonetilleke (Eds.), Computational Feedback Tool for Muscular Rehabilitation Based in Quantitative Analysis of sEMG Signals (pp. 94-101). (Advances in Intelligent Systems and Computing; Vol. 789). https://doi.org/10.1007/978-3-319-94484-5_10

Quizhpe-Cárdenas, Carlos ; Ortiz-Ortiz, Francisco ; Bueno-Palomeque, Freddy et al. / Computational Feedback Tool for Muscular Rehabilitation Based in Quantitative Analysis of sEMG Signals. Computational Feedback Tool for Muscular Rehabilitation Based in Quantitative Analysis of sEMG Signals. editor / Waldemar Karwowski ; Ravindra S. Goonetilleke. 2019. pp. 94-101 (Advances in Intelligent Systems and Computing).

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title = "Computational Feedback Tool for Muscular Rehabilitation Based in Quantitative Analysis of sEMG Signals",

abstract = "Processing sEMG signals in muscle rehabilitation has permitted to measure, register, and use different quantification methods as a biofeedback tool of the techniques used in this area. This study presents a computational tool based in the Wavelet Transform to filter and acquire only the most relevant frequency bands of sEMG signals. Time and frequency analysis were also included. To determine the signal variation of a patient, a comparative analysis can be performed from the beginning of the therapy to a selected date; furthermore, it is possible to compare the behavior and differences among patients. The program was tested by physiotherapists of the IPCA, with sEMG signals of patients with spastic CP. The results delivered by the application agreed with the results of the medical diagnoses, becoming a tool that allows to make decisions about the applied therapies, either to make changes, or to quantify the benefit of this on patients.",

keywords = "Electromyography, Physiotherapy, Quantitative analysis, Wavelet transform",

author = "Carlos Quizhpe-C{\'a}rdenas and Francisco Ortiz-Ortiz and Freddy Bueno-Palomeque and Cabrera, \{Marco Vinicio V{\'a}squez\}",

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doi = "10.1007/978-3-319-94484-5\_10",

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Quizhpe-Cárdenas, C, Ortiz-Ortiz, F, Bueno-Palomeque, F & Cabrera, MVV 2019, Computational Feedback Tool for Muscular Rehabilitation Based in Quantitative Analysis of sEMG Signals. En W Karwowski & RS Goonetilleke (eds.), Computational Feedback Tool for Muscular Rehabilitation Based in Quantitative Analysis of sEMG Signals. Advances in Intelligent Systems and Computing, vol. 789, pp. 94-101, Advances in Intelligent Systems and Computing, Alemania, 1/01/15. https://doi.org/10.1007/978-3-319-94484-5_10

Computational Feedback Tool for Muscular Rehabilitation Based in Quantitative Analysis of sEMG Signals. / Quizhpe-Cárdenas, Carlos; Ortiz-Ortiz, Francisco; Bueno-Palomeque, Freddy et al.
Computational Feedback Tool for Muscular Rehabilitation Based in Quantitative Analysis of sEMG Signals. ed. / Waldemar Karwowski; Ravindra S. Goonetilleke. 2019. p. 94-101 (Advances in Intelligent Systems and Computing; Vol. 789).

Producción científica: Capítulo del libro/informe/acta de congreso › Capítulo

TY - CHAP

T1 - Computational Feedback Tool for Muscular Rehabilitation Based in Quantitative Analysis of sEMG Signals

AU - Quizhpe-Cárdenas, Carlos

AU - Ortiz-Ortiz, Francisco

AU - Bueno-Palomeque, Freddy

AU - Cabrera, Marco Vinicio Vásquez

PY - 2019/1/1

Y1 - 2019/1/1

N2 - Processing sEMG signals in muscle rehabilitation has permitted to measure, register, and use different quantification methods as a biofeedback tool of the techniques used in this area. This study presents a computational tool based in the Wavelet Transform to filter and acquire only the most relevant frequency bands of sEMG signals. Time and frequency analysis were also included. To determine the signal variation of a patient, a comparative analysis can be performed from the beginning of the therapy to a selected date; furthermore, it is possible to compare the behavior and differences among patients. The program was tested by physiotherapists of the IPCA, with sEMG signals of patients with spastic CP. The results delivered by the application agreed with the results of the medical diagnoses, becoming a tool that allows to make decisions about the applied therapies, either to make changes, or to quantify the benefit of this on patients.

AB - Processing sEMG signals in muscle rehabilitation has permitted to measure, register, and use different quantification methods as a biofeedback tool of the techniques used in this area. This study presents a computational tool based in the Wavelet Transform to filter and acquire only the most relevant frequency bands of sEMG signals. Time and frequency analysis were also included. To determine the signal variation of a patient, a comparative analysis can be performed from the beginning of the therapy to a selected date; furthermore, it is possible to compare the behavior and differences among patients. The program was tested by physiotherapists of the IPCA, with sEMG signals of patients with spastic CP. The results delivered by the application agreed with the results of the medical diagnoses, becoming a tool that allows to make decisions about the applied therapies, either to make changes, or to quantify the benefit of this on patients.

KW - Electromyography

KW - Physiotherapy

KW - Quantitative analysis

KW - Wavelet transform

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SN - 9783319944838

T3 - Advances in Intelligent Systems and Computing

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Quizhpe-Cárdenas C, Ortiz-Ortiz F, Bueno-Palomeque F, Cabrera MVV. Computational Feedback Tool for Muscular Rehabilitation Based in Quantitative Analysis of sEMG Signals. En Karwowski W, Goonetilleke RS, editores, Computational Feedback Tool for Muscular Rehabilitation Based in Quantitative Analysis of sEMG Signals. 2019. p. 94-101. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-319-94484-5_10

Computational Feedback Tool for Muscular Rehabilitation Based in Quantitative Analysis of sEMG Signals

Resumen

Serie de la publicación

Conferencia

Areas de Conocimiento del CACES

Acceder al documento

Otros archivos y enlaces

Proyectos

Valoración del movimiento de extensión en el área cervical mediante el análisis de señales electromiográficas en pacientes con PCI espástico

Citar esto