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一种评估虚拟现实手部追踪系统准确性的方法框架:以 Meta Quest 2 为例的研究。

A methodological framework to assess the accuracy of virtual reality hand-tracking systems: A case study with the Meta Quest 2.

机构信息

University of Birmingham, Edgbaston, B15 2TT, Birmingham, UK.

Department of Psychology, Goldsmiths, University of London, SE14 6NW, London, England.

出版信息

Behav Res Methods. 2024 Feb;56(2):1052-1063. doi: 10.3758/s13428-022-02051-8. Epub 2023 Feb 13.

Abstract

Optical markerless hand-tracking systems incorporated into virtual reality (VR) headsets are transforming the ability to assess fine motor skills in VR. This promises to have far-reaching implications for the increased applicability of VR across scientific, industrial, and clinical settings. However, so far, there are little data regarding the accuracy, delay, and overall performance of these types of hand-tracking systems. Here we present a novel methodological framework based on a fixed grid of targets, which can be easily applied to measure these systems' absolute positional error and delay. We also demonstrate a method to assess finger joint-angle accuracy. We used this framework to evaluate the Meta Quest 2 hand-tracking system. Our results showed an average fingertip positional error of 1.1cm, an average finger joint angle error of 9.6 and an average temporal delay of 45.0 ms. This methodological framework provides a powerful tool to ensure the reliability and validity of data originating from VR-based, markerless hand-tracking systems.

摘要

光学无标记手部跟踪系统与虚拟现实 (VR) 耳机相结合,正在改变在 VR 中评估精细运动技能的能力。这有望为 VR 在科学、工业和临床环境中的更广泛应用产生深远影响。然而,到目前为止,关于这些类型的手部跟踪系统的准确性、延迟和整体性能的数据还很少。在这里,我们提出了一种基于目标固定网格的新方法框架,该框架可以轻松应用于测量这些系统的绝对位置误差和延迟。我们还展示了一种评估手指关节角度准确性的方法。我们使用这个框架来评估 Meta Quest 2 手部跟踪系统。我们的结果显示,指尖位置误差的平均值为 1.1cm,手指关节角度误差的平均值为 9.6,平均时间延迟为 45.0ms。这个方法框架为确保源自基于 VR 的无标记手部跟踪系统的数据的可靠性和有效性提供了一个强大的工具。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5125/10830632/7cfa72725e81/13428_2022_2051_Fig1_HTML.jpg

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