Beyond Upright VR: Understanding Direct Interaction and Hand Redirection Strategies in Supine Posture

VR research predominantly focuses on upright postures, such as sitting or standing. Recent research has begun exploring VR use while lying down, revealing numerous benefits. However, existing interaction techniques are tailored for upright postures, creating challenges when applied to a lying-down position. We conducted an experiment to understand user performance across different spatial dimensions (azimuth, inclination, and distance) during a target selection task in a lying-down posture. Our findings revealed that hand movement increases nearly linearly with target offset and that users experience noticeable fatigue and a desire to minimize physical effort. Motivated by these insights, we identified two prevalent hand redirection methods, Go-Go and RBNL, and, based on empirical performance data, developed an empirical data-driven compensation scaling (DDCS) technique that dynamically scales virtual hand movements. Evaluations indicate that both DDCS and RBNL can significantly reduce hand movement and accelerate interaction in supine VR, though at the cost of increased spatial offsets that may compromise precision; however, users generally prioritize comfort over high accuracy. We further discussed the potential reasons and future implications of adopting VR in a supine posture.