Self-Tuning Extended Kalman Filter Parameters to Identify Ankle's Third-Order Mechanics.
J Biomech Eng
; 143(1)2021 01 01.
Article
en En
| MEDLINE
| ID: mdl-32766749
ABSTRACT
The estimation of human ankle's mechanical impedance is an important tool for modeling human balance. This work presents the implementation of a parameter-estimation approach based on a state-augmented extended Kalman filter (AEKF) to infer the ankle's mechanical impedance during quiet standing. However, the AEKF filter is sensitive to the initialization of the noise covariance matrices. In order to avoid a time-consuming trial-and-error method and to obtain a better estimation performance, a genetic algorithm (GA) is proposed to best tune the measurement noise (Rk) and process noise covariances (Q) of the extended Kalman filter (EKF). Results using simulated data show the efficacy of the proposed algorithm for parameter-estimation of a third-order biomechanical model. Experimental validation of these results is also presented. They suggest that age is an influencing factor in the human balance.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Algoritmos
Tipo de estudio:
Prognostic_studies
Idioma:
En
Revista:
J Biomech Eng
Año:
2021
Tipo del documento:
Article
País de afiliación:
México