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Gait Phase Detection for Lower-Limb Exoskeletons using Foot Motion Data from a Single Inertial Measurement Unit in Hemiparetic Individuals

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Gait Phase Detection for Lower-Limb Exoskeletons using Foot Motion Data from a Single Inertial Measurement Unit in Hemiparetic Individuals.pdf (656.59 KB)
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Sánchez Manchola, Miguel D.
Pinto Bernal, María J.
Múnera, Marcela
Cifuentes, Carlos A.
Abstract (Spanish)
Debido al reciente aumento en el uso de exoesqueletos en las extremidades inferiores como alternativa para la marcha rehabilitación, la detección de la fase de la marcha se ha convertido en una característica cada vez más importante en el control de estos dispositivos. Además, se necesitan dispositivos de recuperación altamente funcionales y de bajo costo en el desarrollo. países, ya que se asignan presupuestos limitados específicamente a los avances biomédicos. Para lograr esto Objetivo, este artículo presenta dos algoritmos de partición de la fase de la marcha que utilizan datos de movimiento de un solo Unidad de medida inercial (IMU) colocada en el empeine del pie. Para estos datos, la velocidad angular sagital y se extrajeron señales de aceleración lineal de nueve sujetos sanos y nueve patológicos. sujetos. Patrones de presión de resistencias sensibles a la fuerza (FSR) instrumentadas en una plantilla personalizada Se utilizaron como valores de referencia. El rendimiento de un algoritmo basado en umbrales (TB) y un algoritmo oculto Algoritmo basado en el modelo de Markov (HMM), entrenado mediante métodos estandarizados y específicos de cada tema. enfoques de parámetros, se compararon durante las tareas de caminata en cinta rodante en términos de errores de sincronización y el índice de bondad. Los hallazgos indican que HMM supera a TB para este hardware configuración. Además, el clasificador basado en HMM entrenado mediante un enfoque intrasujeto mostró Excelente confiabilidad para la evaluación del tiempo medio, es decir, su coeficiente de correlación intraclase (CCI) fue mayor que 0,75. En conclusión, el método basado en HMM propuesto aquí se puede implementar para Reconocimiento de la fase de la marcha, como para evaluar la variabilidad de la marcha en pacientes y controlar ortesis robóticas. para rehabilitación de miembros inferiores.
Abstract (English)
Due to the recent rise in the use of lower-limb exoskeletons as an alternative for gait rehabilitation, gait phase detection has become an increasingly important feature in the control of these devices. In addition, highly functional, low-cost recovery devices are needed in developing countries, since limited budgets are allocated specifically for biomedical advances. To achieve this goal, this paper presents two gait phase partitioning algorithms that use motion data from a single inertial measurement unit (IMU) placed on the foot instep. For these data, sagittal angular velocity and linear acceleration signals were extracted from nine healthy subjects and nine pathological subjects. Pressure patterns from force sensitive resistors (FSR) instrumented on a custom insole were used as reference values. The performance of a threshold-based (TB) algorithm and a hidden Markov model (HMM) based algorithm, trained by means of subject-specific and standardized parameters approaches, were compared during treadmill walking tasks in terms of timing errors and the goodness index. The findings indicate that HMM outperforms TB for this hardware configuration. In addition, the HMM-based classifier trained by an intra-subject approach showed excellent reliability for the evaluation of mean time, i.e., its intra-class correlation coefficient (ICC) was greater than 0.75. In conclusion, the HMM-based method proposed here can be implemented for gait phase recognition, such as to evaluate gait variability in patients and to control robotic orthoses for lower-limb rehabilitation.
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24 páginas
URI: https://repositorio.escuelaing.edu.co/handle/001/3340
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