To understand human movement during threat encounters for improving emergency response, this study investigated how environmental, threat, and personal factors influence behavior during failed encounters in an ethical, immersive setting. Twenty-three participants performed a goal-directed task in a virtual reality (VR) environment while avoiding threats, with their full-body movements recorded by a motion capture (MoCap) system. Using unsupervised machine learning on the kinematic data, 22 distinct “movement syllables” were identified, and the analysis focused on the final action executed before capture across 221 unsuccessful encounters. The results showed that failures were most commonly preceded by goal-directed actions like ‘walk’ (~43%) and ‘reach’ (~15%), rather than overt escape attempts like ‘run’ (~4%), with the choice of action being significantly influenced by threat type, visibility, and the presence of a safehouse. The discussion highlights that these findings suggest failures often stem from cognitive lapses in situational awareness rather than an inability to escape, and clustering revealed seven distinct participant behavioural groups, indicating stable, individual coping strategies. This integrated VR/MoCap/ML methodology provides objective insights into behavior under duress, with direct implications for enhancing safety training and crowd simulation models.