Scholarlink

<p>Mixed and augmented reality applications increasingly rely on head-mounted displays, yet systematic methods for quantifying reality distortion remain underdeveloped. This study establishes a multidimensional kinematic framework to identify and measure perceptual distortions induced by optical see-through (Microsoft HoloLens2) and video pass-through (Varjo XR3) architectures. Fifty participants performed three-dimensional reaching tasks under baseline, HoloLens2, and Varjo XR3 conditions. The framework integrated path tortuosity, linear jerk, and trajectory variance as complementary metrics capturing spatial deviation, movement quality, and consistency. Video pass-through systems exhibited significant distortions across all parameters: 11.3% increased tortuosity (p &lt; 0.001), 217% elevated jerk (p = 0.0015), and reduced trajectory consistency (SD = 0.347 vs. 0.203 baseline). Optical see-through preserved near-baseline performance (2.6% tortuosity increase, p = 0.379). The multidimensional kinematic assessment framework demonstrates that conventional velocity-based metrics inadequately capture device-specific distortion patterns, necessitating comprehensive evaluation protocols for clinical XR system selection, while jerk and trajectory variance provide discriminative power for technology evaluation. For precision-critical applications, e.g., surgical guidance, industrial assembly and rehabilitation, this methodological approach enables evidence-based selection between optical architectures based on quantifiable perceptual fidelity rather than subjective assessment. The multidimensional kinematic framework establishes quantitative benchmarks for evaluating perceptual fidelity in mixed reality systems. The multidimensional kinematic assessment framework demonstrates that conventional velocity-based metrics inadequately capture device-specific distortion patterns, necessitating comprehensive evaluation protocols for clinical XR system selection.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Assessment of spatial distortions affecting realism and interaction in mixed and augmented reality

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月25日

查看全文
  • Carlotta Fontana,
  • Michele Guacci,
  • Nicola Cappetti

摘要

Mixed and augmented reality applications increasingly rely on head-mounted displays, yet systematic methods for quantifying reality distortion remain underdeveloped. This study establishes a multidimensional kinematic framework to identify and measure perceptual distortions induced by optical see-through (Microsoft HoloLens2) and video pass-through (Varjo XR3) architectures. Fifty participants performed three-dimensional reaching tasks under baseline, HoloLens2, and Varjo XR3 conditions. The framework integrated path tortuosity, linear jerk, and trajectory variance as complementary metrics capturing spatial deviation, movement quality, and consistency. Video pass-through systems exhibited significant distortions across all parameters: 11.3% increased tortuosity (p < 0.001), 217% elevated jerk (p = 0.0015), and reduced trajectory consistency (SD = 0.347 vs. 0.203 baseline). Optical see-through preserved near-baseline performance (2.6% tortuosity increase, p = 0.379). The multidimensional kinematic assessment framework demonstrates that conventional velocity-based metrics inadequately capture device-specific distortion patterns, necessitating comprehensive evaluation protocols for clinical XR system selection, while jerk and trajectory variance provide discriminative power for technology evaluation. For precision-critical applications, e.g., surgical guidance, industrial assembly and rehabilitation, this methodological approach enables evidence-based selection between optical architectures based on quantifiable perceptual fidelity rather than subjective assessment. The multidimensional kinematic framework establishes quantitative benchmarks for evaluating perceptual fidelity in mixed reality systems. The multidimensional kinematic assessment framework demonstrates that conventional velocity-based metrics inadequately capture device-specific distortion patterns, necessitating comprehensive evaluation protocols for clinical XR system selection.