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Related Experiment Video

Updated: Oct 8, 2025

Experimental Methods to Study Human Postural Control

Experimental Methods to Study Human Postural Control

Published on: September 11, 2019

Comparing system identification techniques for identifying human-like walking controllers.

Dave Schmitthenner¹, Anne E Martin¹

¹Penn State, Mechanical Engineering, University Park, PA, USA.

Royal Society Open Science

|December 24, 2021

Summary

This summary is machine-generated.

Researchers identified human walking controllers using system identification. Surprisingly, a simple linearized model was most accurate for the spring-loaded inverted pendulum (SLIP) model, suggesting linear control for unperturbed human gait.

Keywords:

control dynamics human gait spring-loaded inverted pendulum system identification

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Area of Science:

Biomechanics
Robotics
Control Theory

Background:

Human walking is a complex behavior with an unknown underlying control mechanism.
The spring-loaded inverted pendulum (SLIP) model is a common biomechanical model for analyzing walking.
System identification techniques are crucial for inferring control systems from experimental data.

Purpose of the Study:

To infer a human-like controller for the SLIP model using experimental walking data.
To compare the effectiveness of different system identification techniques for gait control.
To determine if nonlinear control models are necessary for human walking.

Main Methods:

Collected human experimental walking data.
Applied three system identification methods: ordinary least squares (linear), a constrained linear system, and sparse identification of nonlinear dynamics (SINDY).
Validated the inferred controllers in simulation.

Main Results:

All three methods accurately modeled the human gait controller, with errors typically below 10%.
The linearized system model demonstrated the highest accuracy, closely followed by SINDY.
Simulations showed controllers remained accurate, with errors below 10% for most states.

Conclusions:

Linear system identification is sufficient for modeling discrete human gait controllers during unperturbed walking.
Nonlinear system identification techniques may not be necessary for this specific application.
Human control of normal, unperturbed walking appears to be approximately linear.