The Past, Present, and Future of Virtual and Augmented Reality Research: A Network and Cluster Analysis of the Literature

Abstract

The recent appearance of low cost virtual reality (VR) technologies – like the Oculus Rift, the HTC Vive and the Sony PlayStation VR – and Mixed Reality Interfaces (MRITF) – like the Hololens – is attracting the attention of users and researchers suggesting it may be the next largest stepping stone in technological innovation. However, the history of VR technology is longer than it may seem: the concept of VR was formulated in the 1960s and the first commercial VR tools appeared in the late 1980s. For this reason, during the last 20 years, 100s of researchers explored the processes, effects, and applications of this technology producing 1000s of scientific papers. What is the outcome of this significant research work? This paper wants to provide an answer to this question by exploring, using advanced scientometric techniques, the existing research corpus in the field. We collected all the existent articles about VR in the Web of Science Core Collection scientific database, and the resultant dataset contained 21,667 records for VR and 9,944 for augmented reality (AR). The bibliographic record contained various fields, such as author, title, abstract, country, and all the references (needed for the citation analysis). The network and cluster analysis of the literature showed a composite panorama characterized by changes and evolutions over the time. Indeed, whether until 5 years ago, the main publication media on VR concerned both conference proceeding and journals, more recently journals constitute the main medium of communication. Similarly, if at first computer science was the leading research field, nowadays clinical areas have increased, as well as the number of countries involved in VR research. The present work discusses the evolution and changes over the time of the use of VR in the main areas of application with an emphasis on the future expected VR’s capacities, increases and challenges. We conclude considering the disruptive contribution that VR/AR/MRITF will be able to get in scientific fields, as well in human communication and interaction, as already happened with the advent of mobile phones by increasing the use and the development of scientific applications (e.g., in clinical areas) and by modifying the social communication and interaction among people.

Examination of electrodermal and cardio-vascular reactivity in virtual reality through a combined stress induction protocol

“…Research on VR is accelerating rapidly across applications. Illustrating the scope, in a 2018 paper (Cipresso et al 2018) authors have gathered 21667 publications relating to VR within the Web of Science Core collection alone. Authors note an increase in journal publications on the topic as well as an increase in clinical inquiries.…”

Preparing scientists for a visual future

“…Looking ahead, augmented reality (AR) and virtual reality (VR) will have a tremendous impact on scientific visualization, and have already shown immense value to clinicians, neuroscientists, psychologists, biologists, and other researchers [3]. AR and VR are more intuitive to use than flat 2D screens for viewing complex 3D structures, and thereby provide a quicker path to insights [4].…”

An open interface system for non-invasive brain-to-brain free communication between naive human participants

“…Free and open communication is fundamental to modern life, scientific enterprise and democratic discourse. The rising prevalence of technologies such as virtual/augmented reality [1][2][3][4] and artificial intelligence [5; 6] has created new opportunities for hands-free communication and control. Brain-computer interfaces (BCI), which translate measurements of the user's brain activity into computer commands to control external devices [7][8][9][10][11] , present emerging forms of hands-free communication.…”

“…As depicted in Figure 5c, classification accuracy in fact benefited from fewer rather than more harmonics. To assess this statistically, classification accuracy (%) was submitted to a two-way mixed ANOVA with group (high accuracy, low accuracy) and number of harmonics (1,2,3,4,5,6,7,8,9,10) as factors. There were significant main effects of group (F1,15 = 25.95, p < .001, ηp 2 = .63) and number of harmonics (F9,21 = 8.05, p = .005, ηp 2 = .35; Greenhouse-Geisser adjusted, Mauchly's W < .001, X 2 = 263.23, p < .001), but no significant group × number of harmonics interaction (F9,21 = 0.39, p = .388, ηp 2 = .06).…”

“…Increases in classification accuracy. (4) Presenting status characters at the top of each key reduced working memory load as well as the number of saccades required between classification intervals. (5) To reduce potential interference from endogenous alpha oscillations, we used a higher frequency range (10.0 -15.4 Hz rather than 8.0 -15.8 Hz).…”