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Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior

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Synergy Feedback Control Predicts Walking Across Multiple Cycles.

Spencer Williams¹, Geng Li¹, B J Fregly¹

¹Rice Computational Neuromechanics Lab, Department of Mechanical Engineering, Rice University, Houston, TX, United States.

Biorxiv : the Preprint Server for Biology

|March 16, 2026

Summary

This summary is machine-generated.

This study developed a personalized neuromusculoskeletal model for stroke patients, finding that a significant level of feedforward control is crucial for accurately simulating walking dynamics. Sufficient data fitting is also essential for predicting patient-specific motion.

Keywords:

EMG-driven modeling Model Personalization Muscle synergies Neural feedback Neuromusculoskeletal modeling Predictive simulation Stroke Treatment Optimization

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Area of Science:

Biomechanics
Neuroscience
Computational modeling

Background:

Neural feedback is vital for movement control, but neurological disorders disrupt this process.
Existing computational models often lack personalization, limiting their clinical application for movement impairments.

Purpose of the Study:

To develop and evaluate a novel synergy-based feedforward (FF) + feedback (FB) neuromusculoskeletal model.
To create a patient-specific model for an individual post-stroke using personalized walking data.

Main Methods:

Developed a personalized 3D neuromusculoskeletal walking model using experimental data from a post-stroke subject.
Calculated nominal FF synergy controls and scaled them, fitting FB synergy controls to reconstruct muscle activations and joint moments.
Evaluated six FF+FB models in predictive simulations using subject-specific data.

Main Results:

The model with 100% feedforward control most accurately reproduced experimental walking cycles.
Higher levels of feedforward control (75-125%) were necessary for generating near-periodic walking motions with consistent initial conditions.
Lower feedforward levels required substantial divergence in initial conditions for periodic motion.

Conclusions:

Predictive simulations of walking require a minimum threshold of feedforward control.
Accurate prediction of dynamically consistent, patient-specific motion necessitates sufficient fitting data and feedforward control.