[A modeling method for human standing balance system based on T-S fuzzy identification].

Area of Science:

• Biomechanics
• Control Systems Engineering
• Rehabilitation Technology

Background:

• Developing safe and effective passive balance rehabilitation systems requires accurate models of human standing balance.
• Existing models may not fully capture the complex dynamics of balance adjustment in response to external stimuli.

Purpose of the Study:

• To propose a novel mathematical model for human standing balance adjustment.
• To utilize T-S fuzzy identification for modeling balance dynamics.
• To optimize model parameter identification for enhanced accuracy in rehabilitation applications.

Main Methods:

• Human standing balance adjustment was modeled using T-S fuzzy identification.
• Multidimensional motion platform acceleration served as model input.
• Human joint angles were model outputs.
• Artificial bee colony optimization enhanced fuzzy C-means clustering for parameter identification.
• Experimental data from 9 participants were used for training and validation.

Main Results:

• The developed model demonstrated high accuracy in predicting human joint angles.
• Validation using mean square error and cross-correlation confirmed model reliability.
• The optimized fuzzy identification method improved parameter estimation efficiency.

Conclusions:

• The proposed T-S fuzzy identification model provides an accurate and reasonable representation of human standing balance adjustment.
• This model can inform the development of safer training intensities and methods for passive balance rehabilitation systems.
• The integration of artificial bee colony optimization enhances the practical applicability of the model in clinical settings.