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The headset's display offers high-quality resolution and an incredible 110-degree field of view - so users can see better what's around them. The kit also comes with controllers which act as a unique and intuitive extension of the hand to offer even more interactivity during a virtual tour.
The latest generation of virtual and mixed reality hardware has rekindled interest in virtual reality GIS (VRGIS) and augmented reality GIS (ARGIS) applications in health, and opened up new and exciting opportunities and possibilities for using these technologies in the personal and public health arenas. From smart urban planning and emergency training to Pokémon Go, this article offers a snapshot of some of the most remarkable VRGIS and ARGIS solutions for tackling public and environmental health problems, and bringing about safer and healthier living options to individuals and communities. The article also covers the main technical foundations and issues underpinning these solutions.
VRGIS can be seen as an enhanced version of geographical virtual worlds. VRGIS, which merges 3D stereoscopic, VR and GIS technologies, uses footprint files in GIS format for 3D reconstruction [50], and expresses GIS information in the VR domain based on a coupled system; the VRGIS method consists of GIS and VR modules [51]. When operating VRGIS in the virtual environment, users can interact with the system and get feedback from it using different sensing devices. The external world and the system can form a feedback loop through sensing devices.
Thanks to modern multimedia, mass storage technologies and linkages through broadband networks, VRGIS is able to combine remote sensing (RS), aerial photogrammetry, GPS, GIS, city simulation, virtual displays and other technologies to conduct detailed 3D descriptions of a multi-resolution, multi-scale complex geographical environment, with multiple spatio-temporal categories. This is where past, present, and future geographical environments are rendered in a realistic and immersive manner with digital virtual reality via computer networks and other information technologies [51, 52].
The virtual worlds that are explored in VRGIS range from natural landscapes to urban cityscapes. Many VRGIS applications require the worlds to have a certain amount of detail in order to be useful. City planners, for example, need to identify the exact 3D shape of each building to check if several regulations, such as protected views in a city, are met. Energy companies responsible for planning solar installations for greener and more sustainable environments may be interested in the size and slant of city roofs, including the occlusion of roofs by nearby buildings. To meet these requirements, the 3D models that represent these environments should be large and detailed, which makes them extremely complex.
Collaborative mapping in 3D is more difficult than in 2D, because it requires users to have basic knowledge and skills of 3D modelling. Also WGM data are heterogeneous in quality, completeness and accuracy, which makes 3D reconstruction difficult [93]. Yet, if these challenges can be overcome, the idea of a comprehensive user-generated 3D map of the world presents many exciting possibilities. For example, a 3D model of the city of London could be a shared resource for planning, tourism and heritage, or an extremely large user-created fantasy virtual world that can underpin the next generation of massive-multiplayer games [50].
In a modern smart city system, the most basic characteristic of VRGIS is its capacity to visualise 3D details. Users immersed in the virtual environment can test different possibilities and candidate locations for a given task or a new city development plan to decide on the best course of action to take [130, 131]. Planners of new buildings or other facilities can have a comprehensive view of their new development location from various perspectives, including surrounding and nearby buildings. Users, such as city managers, can see the actual landscape of streets, buildings and vehicles, and assess the number of buildings, congestion conditions and light exposure within the vicinity.
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Virtual reality (VR) systems offer a powerful tool for human behavior research. The ability to create three-dimensional visual scenes and to measure responses to the visual stimuli enables the behavioral researcher to test hypotheses in a manner and scale that were previously unfeasible. For example, a researcher wanting to understand interceptive timing behavior might wish to violate Newtonian mechanics so that objects can move in novel 3-D trajectories. The same researcher might wish to collect such data with hundreds of participants outside the laboratory, and the use of a VR headset makes this a realistic proposition. The difficulty facing the researcher is that sophisticated 3-D graphics engines (e.g., Unity) have been created for game designers rather than behavioral scientists. To overcome this barrier, we have created a set of tools and programming syntaxes that allow logical encoding of the common experimental features required by the behavioral scientist. The Unity Experiment Framework (UXF) allows researchers to readily implement several forms of data collection and provides them with the ability to easily modify independent variables. UXF does not offer any stimulus presentation features, so the full power of the Unity game engine can be exploited. We use a case study experiment, measuring postural sway in response to an oscillating virtual room, to show that UXF can replicate and advance upon behavioral research paradigms. We show that UXF can simplify and speed up the development of VR experiments created in commercial gaming software and facilitate the efficient acquisition of large quantities of behavioral research data.
The graphics processes for immersive technologies are significantly more complex than those required for two-dimensional displays. In VR, it is difficult to think of stimuli in terms of a series of colored pixels. The additional complexity includes a need for stimuli to be displayed in apparent 3-D in order to simulate the naturalistic way objects appear to scale, move, and warp according to head position. Unity and other game engines have the capacity to implement the complex render pipeline that can accurately display stimuli in a virtual environment; however, current academic-focused visual display projects may not have the resources to keep up with the evolving demands of immersive technology software. Vizard (WorldViz, 2018), Unreal Engine (Epic Games, 2018), and open-source 3-D game engines such as Godot (Godot, 2018) and Xenko (Xenko, 2018) are also feasible alternatives to Unity, but Unity may still be a primary choice for researchers, because of its ease of use, maturity, and widespread popularity.
UXF is a standalone, generic project, so it does not put any large design constraints on developers using it. This means that UXF does not have to be used in a traditional lab-based setting, with researchers interacting directly with participants; it can also be used for data collection opportunities outside the lab, by embedding experiments within games or apps that a user can partake in at their discretion. Data are then sent to a web server, from which they can later be downloaded and analyzed by researchers (Fig. 5). Recently these cloud-based experiments have become a viable method of performing experiments on a large scale.
The ability to create a virtual swinging room in a VR environment provides a test case for the use of UXF to support behavioral research and provides a proof-of-concept demonstration of how large laboratory experiments can be placed within a nonlaboratory setting. Here, we used the head-tracking function as a proxy measure of postural stability (since decreased stability would be associated with more head sway; Flatters et al., 2014). To test the UXF software, we constructed a simple experiment with both a within-participant component (whether the virtual room was stationary or oscillating) and a between-participant factor (adults vs. children). We then deployed the experiment in a museum with a trained demonstrator and remotely collected data from 100 participants.
Fifty children (all under 16 years of age; mean age = 9.6 years, SD = 2.0 years) and 50 adults (mean age = 27.5 years, SD = 13.2 years) took part in the study. The participants either were recruited from the University of Leeds participant pool (adults) or were attendees at the Eureka! Science Museum (children and adults) and provided full consent. A gaming-grade laptop (Intel Core i5-7300HQ, Nvidia GTX 1060), a VR HMD (Oculus Rift CV1), and the SteamVR application program interface (API), a freely available package independent of UXF (Valve Corp., 2018), were used to present the stimuli and collect data. The HMD was first calibrated using the built-in procedure, which set the virtual floor level to match the physical floor.
We have created an open-source resource that enables researchers to use the powerful gaming engine Unity when designing experiments. We tested the usefulness of UXF by designing an experiment that could be deployed within a museum setting. We found that UXF simplified the development of the experiment and produced measures, in the form of data files that were in a format that made subsequent data analysis straightforward. The data collected were consistent with the equivalent laboratory-based measures (reported over many decades of research), whereby children showed less postural stability than did adults, and whereby both adults and children showed greater sway when the visual information was perturbed. There are likely to be differences in the postural responses of both adults and children within a virtual environment relative to a laboratory setting, and we do not suggest that the data are quantitatively similar between these settings. Nonetheless, these data do show that remotely deployed VR systems can capture age differences and detect the outcomes of an experimental manipulation. 153554b96e
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