Posture-Adaptive Azimuthal Guidance via a Forearm Vibrotactile Interface for VR Navigation

While forearm-worn interfaces are effective for tactile navigation in VR, variations in arm posture during handheld controller use can distort directional perception. This paper presents a posture-adaptive compensation model designed to deliver consistent 2D azimuthal cues across changes in arm posture. In our first experiment, we quantified how the horizontal body-to-forearm angle and wrist rotation affected perceived azimuth, establishing an empirical basis for our computational model. Notably, the perceptual shift remained substantially smaller than the physical change in the body-to-forearm angle, evidencing hybrid integration of body-centered and device-centered reference frames. Based on these findings, we developed a user-adaptive compensation model that combines a representative-posture model with lightweight user adaptation to render intended azimuthal directions across changes in arm posture. Subsequent VR navigation experiments demonstrated that this user-adaptive model significantly improved directional accuracy and navigation efficiency, outperforming the posture-independent baseline by up to 22.4 percentage points. This work establishes a methodology for posture-adaptive, eyes-free navigation by mitigating posture-dependent distortions in directional communication.