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Updated: May 14, 2026

Experimental Methods to Study Human Postural Control

Experimental Methods to Study Human Postural Control

Published on: September 11, 2019

A mathematical model for incorporating biofeedback into human postural control.

Tulga Ersal¹, Kathleen H Sienko

¹Department of Mechanical Engineering, University of Michigan, 2350 Hayward St, Ann Arbor, MI 48109, USA. tersal@umich.edu

Journal of Neuroengineering and Rehabilitation

|February 5, 2013

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This study presents a new, scalable biofeedback model for postural control, treating it as additional torque. The model accurately predicts body sway and center of pressure responses, aiding balance rehabilitation.

Area of Science:

Biomechanics
Robotics
Rehabilitation Engineering

Background:

Biofeedback of body motion aids balance and rehabilitation.
Existing models treat biofeedback as sensory addition, limited to single degree-of-freedom representations.
A scalable method for integrating biofeedback into postural control is needed.

Purpose of the Study:

Develop a scalable biofeedback integration method for postural control, independent of model complexity.
Validate the new model using experimental data from multidirectional perturbations.

Main Methods:

Modeled biofeedback as additional torque to the postural controller.
Applied the model to a vibrotactile device and a two-link multibody model of bipedal stance.
Used experimental data (body sway, COP) from subjects with vestibular deficits for parameterization and validation.

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Published on: April 12, 2016

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Last Updated: May 14, 2026

Experimental Methods to Study Human Postural Control

Experimental Methods to Study Human Postural Control

Published on: September 11, 2019

A Human-machine-interface Integrating Low-cost Sensors with a Neuromuscular Electrical Stimulation System for Post-stroke Balance Rehabilitation

A Human-machine-interface Integrating Low-cost Sensors with a Neuromuscular Electrical Stimulation System for Post-stroke Balance Rehabilitation

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Main Results:

The model achieved low average errors (0.24° for body sway, 0.39 cm for COP).
High cross-correlation values (0.97) were observed for both body sway and COP.
The model accurately captured experimental response trajectories across various conditions.

Conclusions:

The developed biofeedback model effectively captures experimental data with high accuracy.
Biofeedback can be modeled as additional torque without sensory reweighting.
The scalable model is applicable to diverse balance and rehabilitation scenarios, confirming display resolution's nuanced effect.