Abstract

Humans adapt their postural control strategies in response to fear, but traditional sway metrics cannot directly reveal the underlying control objectives. We out line a preprocessing pipeline to enable inference of latent cost functions through our ongoing inverse optimal control (IOC) analysis. We exposed participants to ground (GC) and height (HC) conditions in virtual reality, while recording joint kinematics using Kinect-based motion capture. After aligning and denoising the data, we extracted joint angles (hip, knee, ankle) and computed summary metrics such as Mean, RMS, and Mean Power Frequency (MPF). Using Bayesian estimation, we found condition-dependent shifts in joint angle distributions, in cluding reduced hip flexion and increased ankle stability under height. Our findings provide evidence of postural adaptation under perceived threat and lay the ground work for the modeling of control strategies that govern balance in fear-inducing environments.