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

Resultado de la investigación: Capítulo del libro/informe/acta de congresoCapítulo

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 originalInglés
Título de la publicación alojadaComputational Feedback Tool for Muscular Rehabilitation Based in Quantitative Analysis of sEMG Signals
EditoresWaldemar Karwowski, Ravindra S. Goonetilleke
Páginas94-101
Número de páginas8
ISBN (versión digital)9783319944838
DOI
EstadoPublicada - 1 ene 2019
EventoAdvances in Intelligent Systems and Computing - , Alemania
Duración: 1 ene 2015 → …

Serie de la publicación

NombreAdvances in Intelligent Systems and Computing
Volumen789
ISSN (versión impresa)2194-5357

Conferencia

ConferenciaAdvances in Intelligent Systems and Computing
País/TerritorioAlemania
Período1/01/15 → …

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