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.
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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: Goonetilleke, R., Karwowski, W. (eds) Advances in Physical Ergonomics & Human Factors. AHFE 2018. Advances in Intelligent Systems and Computing, vol 789. Springer, Cham. https://doi.org/10.1007/978-3-319-94484-5_10
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DOI: https://doi.org/10.1007/978-3-319-94484-5_10
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