Classification of the Angular Position During Wrist Flexion-extension Based on EMG Signals

• Published in: Ingeniería y universidad Vol. 25
• Main Authors: Fajardo-Perdomo, María Alexandra, Guardo-Gómez, Verónica, Orjuela-Cañón, Alvaro David, Ruiz-Olaya, Andrés Felipe
• Format: Journal Article
• Language: English
• Published: Bogotá Editorial Pontificia Universidad Javeriana 2021
• Subjects: Algorithms; Angular position; Artificial neural networks; Autoregressive models; Autoregressive processes; Classification; Comparative studies; Differentiation; Electrodes; Electromyography; electromyography signals; Fourier transforms; intencionalidad de movimiento; Machine learning; machine learning techniques; Movement intent; Multilayer perceptrons; Myoelectricity; Neural networks; Parameters; Pattern recognition; Pattern recognition systems; Range of motion; reconocimiento de patrones; redes neuronales artificiales; Rehabilitation; señales de electromiografía; Signal processing; técnicas de aprendizaje automático; Wavelet transforms; Wrist
• ISSN: 0123-2126; 2011-2769
• DOI: 10.11144/Javeriana.iued25.capd

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.