Palabras clave
señales de electromiografía
reconocimiento de patrones
técnicas de aprendizaje automático
redes neuronales artificiales
Cómo citar
Resumen
Objetivo: evaluar un grupo de características en un algoritmo de reconocimiento de patrones mioeléctricos para discriminar cinco posiciones angulares de la muñeca durante los movimientos de flexoextensión. Materiales y métodos: se realizó una configuración experimental para adquirir EMG y ángulo articular de la muñeca, relacionado con los movimientos de flexión-extensión. Después de eso, se implementó un algoritmo de reconocimiento de patrones mioeléctricos basado en una red neuronal artificial de perceptrón multicapa (ANN). Se emplearon tres grupos diferentes: características de dominio de tiempo, parámetros de modelos autorregresivos (AR) y representación de frecuencia de tiempo usando la transformación Wavelet (WT). Resultados y discusión: los resultados experimentales de 10 sujetos sanos indican que los coeficientes de los modelos AR ofrecen los mejores parámetros para la clasificación, alcanzando una tasa de discriminación del 78 % en cinco posiciones angulares estudiadas. La combinación de frecuencia y frecuencia de tiempo proporcionó una tasa de discriminación que alcanzó el 82 %. Conclusiones: se ha realizado un estudio comparativo de grupos de características que permiten discriminar la posición angular, a nivel del movimiento de flexo-extensión de la muñeca, mediante un algoritmo basado en reconocimiento de patrones de las señales EMG. El método tiene potencial aplicación en el ámbito de la ingeniería de rehabilitación, para la detección de la intencionalidad de movimiento del usuario.
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Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.
Derechos de autor 2021 Alvaro David Orjuela-Cañón