In this paper, we propose a new interface for virtual reality headset: a touchpad in front of the headset. To demonstrate the feasibility of the front touch interface, we built a prototype device, explored VR UI design space expansion, and performed various user studies. We started with preliminary tests to see how intuitively and accurately people can interact with the front touchpad. Then, we further experimented various user interfaces such as a binary selection, a typical menu layout, and a keyboard. Two-Finger and Drag-n-Tap were also explored to find the appropriate selection technique. As a low-cost, light-weight, and in low power budget technology, a touch sensor can make an ideal interface for mobile headset. Also, front touch area can be large enough to allow wide range of interaction types such as multi-finger interactions. With this novel front touch interface, we paved a way to new virtual reality interaction methods.
There are a plethora of research and industry products on virtual reality devices and interactions. However, an ability to provide precise and intuitive input signal is still identified as an issue among the VR users @cite_23 . In this section, we address how prior studies approached to solve selection tasks, especially with the menu interface, and text-entry.
@cite_23
2095128833
Virtual reality applications are emerging into various regions of research and entertainment. Although visual and acoustic capabilities are already quite impressive, a wide range of users still criticizes the user interface. Frequently complex and very sensitive input devices are being used, although simple gestures would be preferred. While gesture recognition systems are quite common, see Nintendo's Wii mote, a CAVE has further challenges, as the person can be located in any random position and the gestures are not being performed related to a common fixpoint. Applying an infrared tracking system it is possible to reliably locate the hand and compute 3D trajectories. These are then further analyzed with a Long Short-Term Memory approach, which is able to model sequences of variable length with a higher reliability than HMMs.