Object Manipulation in Virtual Reality Under Increasing Levels of Translational Gain

Room-scale Virtual Reality (VR) has become an affordable consumer reality, with applications ranging from entertainment to productivity. However, the limited physical space available for room-scale VR in the typical home or office environment poses a significant problem. To solve this, physical spaces can be extended by amplifying the mapping of physical to virtual movement (translational gain). Although amplified movement has been used since the earliest days of VR, little is known about how it influences reach-based interactions with virtual objects, now a standard feature of consumer VR. Consequently, this paper explores the picking and placing of virtual objects in VR for the first time, with translational gains of between 1x (a one-to-one mapping of a 3.5m*3.5m virtual space to the same sized physical space) and 3x (10.5m*10.5m virtual mapped to 3.5m*3.5m physical). Results show that reaching accuracy is maintained for up to 2x gain, however going beyond this diminishes accuracy and increases simulator sickness and perceived workload. We suggest gain levels of 1.5x to 1.75x can be utilized without compromising the usability of a VR task, significantly expanding the bounds of interactive room-scale VR

Comparing Various Locomotion Methods within Virtual Environments

Two inexpensive methods of exploring a virtual environment are walking in place (WIP) and arm swinging. These techniques are compelling because they strike a balance between space requirements, cost, and proprioceptive cues. They seem to provide better spatial awareness of a virtual environment than other inexpensive virtual navigation techniques such as joysticks or controllers. On the other hand, they are much cheaper and require less space than tracking systems. In our prior work, we had success in implementing a WIP method using an inexpensive Nintendo Wii Balance Board. We showed that participants' spatial orientation was the same as normal walking and superior to joystick navigation. We seek to extend our previous work utilizing the Myo armband{ an inexpensive wearable device (199 USD) with electromyography sensors, gyroscopes, and accelerometers. We previously found that our arm swinging method outperforms a simple joystick and that spatial orientation is comparable to physically walking on foot. In this work, we compare physical locomotion to both arm swinging and WIP. We implement these methods with Myo armbands. Both algorithms let users freely explore an HMD-based virtual environment. We tested users' spatial orientation and distance estimation. Interestingly, our mean turning angle errors were higher than those in our previous studies. Also notable is that users performed better at blind walking in the WIP condition than in physical locomotion