Self-Tuning Extended Kalman Filter Parameters to Identify Ankle's Third-Order Mechanics

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 sta...

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Bibliographic Details
Published inJournal of biomechanical engineering Vol. 143; no. 1
Main Authors Coronado, E, González, A, Cárdenas, A, Maya, M, Chiovetto, E, Piovesan, D
Format Journal Article
LanguageEnglish
Published United States 01.01.2021
Subjects
Adult
Algorithms
Ankle - physiology
Ankle Joint - physiology
Biomechanical Phenomena
Humans
Male
Mechanical Phenomena
Models, Biological
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Summary: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.
ISSN:1528-8951
DOI:10.1115/1.4048042