Addressing virtual reality misclassification: A hardware‐based qualification matrix for virtual reality technology

Abstract

Through its unique sensory synchronized design, virtual reality (VR) provides a convincing, user-centred experience of highly controllable scenarios. Importantly, VR is a promising modality for healthcare, where treatment efficacy has been recognized for a range of conditions. It is equally valuable across wider research disciplines. However, there is a lack of suitable criteria and consistent terminology with which to define VR technology. A considerable number of studies have misclassified VR hardware (e.g. defining laptops as VR), hindering validity and research comparisons. This review addresses these limitations and establishes a standardized VR qualification framework. As a result of a comprehensive theoretical and literature review, the hardware-based VR qualification matrix is proposed.The matrix criteria consist of (1) three-dimensional (3D) synchronized sensory stimulation; (2) degrees of freedom tracking; and (3) visual suppression of physical stimuli. To validate the model and quantify the current scale/diversity of VR misclassification, a 2019 sectional review of health-related studies was conducted.Of the 115 studies examined against standardized criteria, 35.7% utilized VR, 31.3% misclassified VR, 18.3% were considered quasi-VR, and 14.8% omitted critical specifications. The proposed model demonstrates good validity and reliability for qualifying and classifying VR.

Are Australian Mental Health Services Ready for Therapeutic Virtual Reality? An Investigation of Knowledge, Attitudes, Implementation Barriers and Enablers

“…Moreover, the literature to date has suffered from ambiguous terminology, leading to inadequate specification and misclassification of VR, which could diminish literature validity and provider confidence in therapeutic VR. Thus, as the field progresses, it stands to benefit from greater standardization of VR, for instance, with Takac et al's (65) proposed hardware-based VR qualification matrix, as well as development of a therapeutic VR resource directory. A similar initiative funded by the Australian government (i.e., e-Mental Health in Practice) successfully raised awareness of evidence-based digital mental health interventions among primary healthcare providers.…”

Potential of virtual reality to meaningfully engage adults living with dementia in care settings: A scoping review

“…The most common lens used to define VR was personal experience based on the level of immersion in the virtual world using the following categories: fully immersive, semi‐immersive and non‐immersive. Takac et al (2021) argue that these categories rely on a subjective interpretation of what is considered immersive, which contributes to the inconsistencies of VR classification. The fact that VR was also defined based on its technical specifications, relating to the software and hardware used, further contributed to these inconsistencies and misclassifications of VR in the literature (e.g., Matsangidou et al, 2022; Optale et al, 2010; Yahara et al, 2021).…”

“…Their proposed categories of non-immersive, semi-immersive and fully immersive are generally accepted within the literature and often used to classify VR. However, as Takac et al (2021) explains, these categories rely on the subjective interpretation of what is considered to be immersive, resulting in a tendency to misclassify certain technology as VR in the literature.…”

The Application of Virtual Reality Technology in Sports Psychology: Theory, Practice, and Prospect

“…In fact, the POSIT algorithm is one of the iterative algorithms for the PNP problem mentioned above [ 29 ]. First, the initial value of the pose parameters of the three-dimensional object (POS, Pose from Orthography and Scaling algorithm) is obtained through the relationship between orthogonal projection and size transformation [ 30 ]. With the new pose measurement parameters, the POS algorithm is re-run, after repeated iterations, until the required accuracy is met in an algorithm flow.…”