arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2103.07757cs.HC
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面向延展实境的基于模型的任务分析与大规模视频远程评测方法研究

Model-based Task Analysis and Large-scale Video-based Remote Evaluation Methods for Extended Reality Research

Yalda Ghasemi, Heejin Jeong

摘要:

在本文中,我们介绍了两种远程延展实境(XR)研究方法,它们可以克服基于实验室的受控实验的局限性,特别是在COVID-19大流行期间:(1)基于预测模型的任务分析和(2)基于大规模视频的远程评估。我们使用了一个盒子堆叠任务,包括三种交互方式-两种多模态的基于凝视的交互以及一种单模态的基于手的交互,这被定义为我们的基线。对于第一次评估,通过分析任务来执行基于GOMS的任务分析,以了解XR中的人类行为并预测任务执行时间。对于第二次评估,使用一系列第一人称视角视频进行在线调查,其中用户使用三种交互方式执行相应的任务。共有118名参与者被要求根据他们的判断比较互动模式。两个标准的问卷被用来衡量感知的工作量和可用性的方式。

英文摘要:

In this paper, we introduce two remote extended reality (XR) research methods that can overcome the limitations of lab-based controlled experiments, especially during the COVID-19 pandemic: (1) a predictive model-based task analysis and (2) a large-scale video-based remote evaluation. We used a box stacking task including three interaction modalities - two multimodal gaze-based interactions as well as a unimodal hand-based interaction which is defined as our baseline. For the first evaluation, a GOMS-based task analysis was performed by analyzing the tasks to understand human behaviors in XR and predict task execution times. For the second evaluation, an online survey was administered using a series of the first-person point of view videos where a user performs the corresponding task with three interaction modalities. A total of 118 participants were asked to compare the interaction modes based on their judgment. Two standard questionnaires were used to measure perceived workload and the usability of the modalities.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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