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

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.