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

Research Group in Biomedical Engineering (GIIB)

Research output: Chapter in Book/Report/Conference proceeding › Chapter

1 Scopus citations

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.

Original language	English
Title of host publication	Computational Feedback Tool for Muscular Rehabilitation Based in Quantitative Analysis of sEMG Signals
Editors	Waldemar Karwowski, Ravindra S. Goonetilleke
Pages	94-101
Number of pages	8
ISBN (Electronic)	9783319944838
DOIs	https://doi.org/10.1007/978-3-319-94484-5_10
State	Published - 1 Jan 2019
Event	Advances in Intelligent Systems and Computing - , Germany Duration: 1 Jan 2015 → …

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	789
ISSN (Print)	2194-5357

Conference

Conference	Advances in Intelligent Systems and Computing
Country/Territory	Germany
Period	1/01/15 → …

Keywords

Electromyography
Physiotherapy
Quantitative analysis
Wavelet transform

CACES Knowledge Areas

419A Medical Diagnostic and Treatment Technology

Access to Document

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

Cite this

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. In 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\}",

year = "2019",

month = jan,

day = "1",

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

language = "English",

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series = "Advances in Intelligent Systems and Computing",

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editor = "Waldemar Karwowski and Goonetilleke, \{Ravindra S.\}",

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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. in 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, Germany, 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).

Research output: Chapter in Book/Report/Conference proceeding › Chapter

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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UR - http://www.mendeley.com/research/computational-feedback-tool-muscular-rehabilitation-based-quantitative-analysis-semg-signals

U2 - 10.1007/978-3-319-94484-5_10

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

M3 - Chapter

SN - 9783319944838

T3 - Advances in Intelligent Systems and Computing

SP - 94

EP - 101

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

A2 - Karwowski, Waldemar

A2 - Goonetilleke, Ravindra S.

T2 - Advances in Intelligent Systems and Computing

Y2 - 1 January 2015

ER -

Quizhpe-Cárdenas C, Ortiz-Ortiz F, Bueno-Palomeque F, Cabrera MVV. Computational Feedback Tool for Muscular Rehabilitation Based in Quantitative Analysis of sEMG Signals. In Karwowski W, Goonetilleke RS, editors, 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

Abstract

Publication series

Conference

Keywords

CACES Knowledge Areas

Access to Document

Other files and links

Assessment of extension movement in the cervical area through the analysis of electromyographic signals in patients with spastic CP

Cite this

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

Abstract

Publication series

Conference

Keywords

CACES Knowledge Areas

Access to Document

Other files and links

Projects

Assessment of extension movement in the cervical area through the analysis of electromyographic signals in patients with spastic CP

Cite this