Publicado

2019-01-01

Towards the reduction of the effects of muscle fatigue on myoelectric control of upper limb prostheses

Hacia la reducción de los efectos de la fatiga muscular en el control mioeléctrico de prótesis de miembro superior

Palabras clave:

electromyography, myoelectric control, muscle fatigue, upper limb prostheses, pattern recognition (en)
electromiografía, control mioeléctrico, prótesis de miembro superior, reconocimiento de patrones (es)

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This paper presents an investigation focused on the impact of muscle fatigue on a pattern recognition scheme for myoelectric control that uses three features sets and a Linear Discriminant Analysis classifier. Separability and repeatability between classes were used to evaluate the features changes while muscle fatigue was induced. Results show that while muscle fatigue is increasing over time, both separability and repeatability of the classes decrease. Finally two training schemes that use data acquired under fatigue, multiconditional training and selective classification, were evaluated using the Total Error Rate (TER). Results indicate that, when LDA classifier was trained whit no-fatigue, moderated fatigue and fatigue data, TER decreased to moderated and fatigue data, but increased to no-fatigue data. On the other hand, using three LDA classifiers to each of the condition, TER decreased to 9.26 % and 11 % in moderated fatigue and fatigue cases, while no-fatigue case was not affected.

Este artículo presenta una investigación centrada en el impacto de la fatiga muscular en un esquema de reconocimiento de patrones para el control mioeléctrico que utiliza tres conjuntos de características y un clasificador análisis discriminante lineal. Los cambios en las características mientras se inducía la fatiga muscular se evalúan mediante la separabilidad y la repetibilidad entre las clases. Los resultados muestran que mientras la fatiga muscular aumenta con el tiempo, tanto la separabilidad como la repetibilidad disminuyen. Finalmente se evaluaron, mediante tasa de error total (TER), dos esquemas de entrenamiento que usan datos adquiridos bajo fatiga: entrenamiento multicondición y clasificación selectiva. Los resultados indican que, utilizando entrenamiento multicondición, el TER disminuyó para fatiga moderada y fatiga, pero aumentó para no-fatiga. Por otro lado, al usar clasificación selectiva, TER disminuyó a 9.26% y 11% en casos fatiga moderada y fatiga, mientras que la condición no-fatiga no se vio afectada.

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