Clasificación de la posición angular en flexoextensión de la muñeca a partir de señales EMG

María Alexandra Fajardo-Perdomo, Verónica Guardo-Gómez, Alvaro David Orjuela-Cañon, Andrés Felipe Ruiz-Olaya

Resultado de la investigación: Contribución a una revistaArtículorevisión exhaustiva

Resumen

Objective: To evaluate a group of features in a myoelectric pattern recognition algorithm to differentiate between five angular positions of the wrist during flexion-extension movements. Materials and Methods: An experimental configuration was made to capture the EMG and wrist joint angle related to flexion-extension movements. After that, a myoelectric pattern recognition algorithm based on a multilayer perceptron artificial neural network (ANN) was implemented. Three different groups were used: Time domain characteristics, autoregressive (AR) model parameters, and representation of time frequency using Wavelet transform (WT). Results and Discussion: The experimental results of 10 healthy subjects indicate that the coefficients of the AR models offer the best parameters for classification, with a differentiation rate of 78% for the five angular positions studied. The combination of frequency and time frequency resulted in a differentiation rate that reached 82%. Conclusions: An algorithm based on pattern recognition of EMG signals was used to carry out a comparative study of groups of features that allow for the differentiation of the angular position of the wrist in terms of flexion-extension movements. The method has the potential for application in the field of rehabilitation engineering to detect the user’s movement intent.
Título traducido de la contribuciónClassification of the angular position during wrist flexion extension based on emg signals
Idioma originalEspañol
PublicaciónIngenieria y Universidad
Volumen25
DOI
EstadoPublicada - 2021

All Science Journal Classification (ASJC) codes

  • Ingeniería (todo)

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