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.

中文翻译：

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解决虚拟现实错误分类：虚拟现实技术的基于硬件的资格矩阵

通过其独特的感官同步设计，虚拟现实 (VR) 为高度可控的场景提供了令人信服的、以用户为中心的体验。重要的是，VR 是一种很有前景的医疗保健方式，其治疗效果已被认可适用于多种疾病。它在更广泛的研究学科中同样有价值。然而，缺乏合适的标准和一致的术语来定义 VR 技术。大量研究错误地将 VR 硬件分类（例如将笔记本电脑定义为 VR），从而阻碍了有效性和研究比较。本次审查解决了这些限制并建立了标准化的 VR 资格框架。作为综合理论和文献综述的结果，提出了基于硬件的 VR 资格矩阵。矩阵标准包括 (1) 三维 (3D) 同步感官刺激；(2)自由度跟踪；(3) 视觉抑制物理刺激。为了验证模型并量化当前 VR 错误分类的规模/多样性，我们对 2019 年的健康相关研究进行了部分审查。在根据标准化标准检查的 115 项研究中，35.7% 使用 VR，31.3% 错误分类 VR，18.3% 被认为是准 VR，14.8% 省略了关键规范。所提出的模型证明了对 VR 进行资格和分类的良好有效性和可靠性。2019 年对健康相关研究进行了部分审查。在根据标准化标准检查的 115 项研究中，35.7% 使用 VR，31.3% 错误分类 VR，18.3% 被认为是准 VR，14.8% 省略了关键规范。所提出的模型证明了对 VR 进行资格和分类的良好有效性和可靠性。2019 年对健康相关研究进行了部分审查。在根据标准化标准检查的 115 项研究中，35.7% 使用 VR，31.3% 错误分类 VR，18.3% 被认为是准 VR，14.8% 省略了关键规范。所提出的模型证明了对 VR 进行资格和分类的良好有效性和可靠性。