Simulation-Oriented Parameterization of Virtual Human Head-Orientation Behavior Using Wearable IMU Data

Abstract

Realistic head-orientation behavior contributes to the credibility of virtual human agents in scenario-based simulations. Conventional finite state machine (FSM) agents often rely on manually defined perception rules, producing repetitive head motion patterns that do not reflect action-dependent human behavior. This study derives action-specific head-orientation parameters from head-mounted inertial measurement unit (IMU) recordings collected from 20 participants across six actions and 600 recording sessions. Preprocessed yaw trajectories were segmented into steady-orientation, scanning, and rapid-turn states and summarized using scan amplitude, scan period, directional bias, head-orientation switching interval, reorientation magnitude, and orientation-state proportion. The resulting profiles were applied to an FSM-based virtual human agent. Participant-independent validation used a 20-fold leave-one-subject-out procedure with comparisons against a fixed rule-based agent and a common stochastic agent using one pooled profile for all actions. The action-specific agent outperformed the common stochastic agent across all evaluation metrics. Compared with the rule-based agent, the action-specific agent showed lower switching interval and orientation-state proportion deviations, whereas the rule-based agent showed higher yaw-distribution similarity. Parameter-wise replacement analysis indicated that angular parameters mainly affected yaw-distribution agreement, while switching interval and orientation-state proportion governed their corresponding temporal and compositional measures. The method improves action-dependent temporal and compositional fidelity while retaining explicit FSM control.