Effects of Postures on Identifying Users for Selection-Based Behavioral Authentication in Virtual Reality

Behavioral authentication has become increasingly popular as a natural method for authentication in Virtual Reality (VR). However, existing studies often overlook the fact that users may perform behavioral authentication in different postures (i.e., sitting, standing, reclining) during VR use. Therefore, understanding how posture variations affect classification accuracy is crucial for designing posture-robust systems. In this study, we conducted a controlled experiment (N = 30) to investigate the impact of posture on classification accuracy during a target-selection task. We collected behavioral trajectory data and analyzed it using multivariate time series classification algorithms, addressing authentication performance under three different postures. In a within-posture authentication, reclining took longer but achieved the highest classification accuracy, with an interaction effect between posture and target vertical layout. In cross-posture authentication, transfers from sitting to standing/reclining were more effective than direct transfers between standing and reclining, with vertical layout crucial for classification accuracy. In mixed-posture training, the cross-posture classification accuracy increased, particularly when standing and reclining data were combined to help the model indirectly learn features of sitting posture. These findings provide valuable insights for designing tasks and data collection strategies that support the development of robust cross-posture authentication systems.