Estimating continuous and intermittent feedback models of postural control using the least squares method.

Area of Science:

• Biomechanics
• Systems Neuroscience
• Human Motor Control

Background:

• Biomechanical models are crucial for understanding postural control and balance mechanisms.
• Identifying parameters for continuous and intermittent controller models from experimental data across diverse populations and sensory conditions is underexplored.

Purpose of the Study:

• To estimate and compare parameters of continuous and intermittent feedback models of postural control.
• To investigate parameter identification using ankle torque signals and least squares (LS)-based methods.
• To analyze model performance across young and older adults under varied sensory conditions.

Main Methods:

• Employed ankle torque signals and least squares (LS)-based methods (LS, non-negative LS, bounded-variable LS) to estimate model parameters.
• Utilized sway data from young and older adults during quiet standing under varied sensory conditions.
• Validated model accuracy by simulating human sway patterns.

Main Results:

• LS-based methods achieved high coefficients of determination for both continuous and intermittent models.
• Passive stiffness in intermittent models was consistent across sensory conditions.
• Active parameters varied for both models, indicating adaptability, and simulations reproduced human sway patterns accurately.

Conclusions:

• The employed parameter identification techniques are effective for continuous and intermittent postural control models.
• Estimated model parameters demonstrate adaptability to different sensory inputs and age-related differences.
• These findings advance the understanding of the mechanisms underlying human postural control and balance.