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

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 %.

• Autores: Fajardo Perdomo, María Alexandra; Guardo Gómez, Verónica; Orjuela Cañón, Álvaro David; Ruíz Olaya, Andrés Felipe
• Tipo de recurso: Article of journal
• Fecha de publicación: 2021
• Institución: Escuela Colombiana de Ingeniería Julio Garavito
• Repositorio: Repositorio Institucional ECI
• Idioma: eng
• OAI Identifier: oai:repositorio.escuelaing.edu.co:001/3247
• Acceso en línea: https://repositorio.escuelaing.edu.co/handle/001/3247; https://repositorio.escuelaing.edu.co/
• Palabra clave: Medicina física; Medicine physical; Articulaciones; Joints; Biomecánica; Biomechanics; Intencionalidad de movimiento; Señales de electromiografía; Reconocimiento de patrones; Técnicas de aprendizaje automático; Redes neuronales artificiales; Movement intent; Electromyography signals; Pattern recognition; Machine learning techniques; artificial neural networks
• Rights: closedAccess
• License: http://purl.org/coar/access_right/c_14cb