An Adaptive Hammerstein Model for FES-Induced Torque Prediction Based on Variable Forgetting Factor Recursive Least Squares Algorithm

Modeling the muscle response to functional electrical stimulation (FES) is an important step during model-based FES control system design. The Hammerstein structure is widely used in simulating this nonlinear biomechanical response. However, a fixed relationship cannot cope well with the time-varyin...

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Vydáno v:IEEE transactions on neural systems and rehabilitation engineering Ročník 32; s. 1109 - 1118
Hlavní autoři: Yang, Qinlian, Li, Yingqi, Li, You, Zheng, Manxu, Song, Rong
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Accuracy
Adaptation models
Algorithms
Ankle
Biological neural networks
Biomechanics
Control systems
Control systems design
Electric Stimulation - methods
Electric Stimulation Therapy - methods
Electrical stimulation
Electrical stimuli
Functional electrical stimulation
Goodness of fit
Hammerstein model
Humans
Iron
Least squares
Least-Squares Analysis
Mathematical models
Muscle contraction
muscle model
Muscle, Skeletal - physiology
Muscles
Muscular fatigue
Nonlinear response
Parameters
Predictive models
Stability analysis
Stimulation
system identification
Torque
ISSN:1534-4320, 1558-0210, 1558-0210
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Shrnutí:Modeling the muscle response to functional electrical stimulation (FES) is an important step during model-based FES control system design. The Hammerstein structure is widely used in simulating this nonlinear biomechanical response. However, a fixed relationship cannot cope well with the time-varying property of muscles and muscle fatigue. In this paper, we proposed an adaptive Hammerstein model to predict ankle joint torque induced by electrical stimulation, which used variable forgetting factor recursive least squares (VFFRLS) method to update the model parameters. To validate the proposed model, ten healthy individuals were recruited for short-duration FES experiments, ten for long-duration FES experiments, and three stroke patients for both. The isometric ankle dorsiflexion torque induced by FES was measured, and then the test performance of the fixed-parameter Hammerstein model, the adaptive Hammerstein model based on fixed forgetting factor recursive least squares (FFFRLS) and the adaptive Hammerstein model based on VFFRLS was compared. The goodness of fit, root mean square error, peak error and success rate were applied to evaluate the accuracy and stability of the model. The results indicate a significant improvement in both the accuracy and stability of the proposed adaptive model compared to the fixed-parameter model and the adaptive model based on FFFRLS. The proposed adaptive model enhances the ability of the model to cope with muscle changes.
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ISSN:1534-4320
1558-0210
1558-0210
DOI:10.1109/TNSRE.2024.3371465