Effects of Arm Posture and Speed Estimation Methods on the Performance and User Preference of Virtual Locomotion Using Arm Swing
Jingbo Zhao, Zhijie Li, Mingjun Shao, BoYu Gao · International Journal of Human-Computer Interaction · 2025
Arm swing is a viable approach for locomotion in virtual reality (VR). However, previous studies have not investigated the effects of arm posture (straight arms and bent arms) and speed estimation methods on speed estimation error, locomotion performance, and user preference. We proposed two data-driven approaches for virtual locomotion based on a one-dimensional convolutional neural network (1-D CNN) and the support vector regression (SVR), respectively, and compared their speed estimation errors using treadmill walking data for both bent arms and straight arms. We then compared the locomotion performance and user preference of the two methods against two existing virtual locomotion methods through virtual locomotion tasks. Experimental results suggest that it is necessary to consider the influence of arm posture and speed estimation methods when designing virtual locomotion methods based on arm swing. The results of this work provide insights into the research and the design of arm-swing-based locomotion interfaces.