Modeling Head-Neck Dynamics under Lateral Perturbations Using MPC to Mimic CNS postural stabilization strategy

TL;DR

This paper presents an MPC-based model to simulate head-neck postural responses to lateral perturbations, aiding in assessing autonomous vehicle comfort by mimicking CNS stabilization strategies.

Contribution

It extends existing models with experimental validation, showing muscle effort and somatosensory feedback effectively replicate human head-neck responses without complex integrators.

Findings

Model accurately reproduces human head-neck responses

Muscle effort and somatosensory feedback are key for dynamic fit

Model helps assess vehicle comfort under unexpected movements

Abstract

Automated vehicles will allow occupants to engage in non-driving tasks, but limited visual cues will make them vulnerable to unexpected movements. These unpredictable perturbations create a "surprise factor," forcing the central nervous system to rely on compensatory postural adjustments, which are less effective, and are more likely to trigger sensory conflicts. Since the head is a key reference for sensory input (vestibular and vision), models accurately capturing head-neck postural stabilization are essential for assessing AV comfort. This study extends an existing model predictive control-based framework to simulate head-neck postural control under lateral perturbations. Experimental validation against human data demonstrates that the model can accurately reproduce dynamic responses during lateral trunk perturbations. The results show that muscle effort combined with partial somatosensory feedback provides the best overall dynamic fit without requiring corrective relative and global head orientation integrators for posture.