论文标题
使用手持和凝视在沉浸式AR中的垂直分层菜单选择中预测人类绩效
Predicting Human Performance in Vertical Hierarchical Menu Selection in Immersive AR Using Hand-gesture and Head-gaze
论文作者
论文摘要
目前，针对沉浸式增强现实（AR）应用程序设计用户界面（UI）的指南有限。设计师必须反思他们为台式机和移动应用程序设计UI的经验，并猜测UI将如何影响AR用户的性能。在这项工作中，我们介绍了一个预测模型，用于确定用户对目标UI的性能，而无需参与者参与用户研究。该模型对参与者对客观绩效指标的反应进行了培训，例如使用层次下拉菜单，例如消耗的耐力（CE）和指向时间（PT）。通过从词汇数据库WordNet中包含的单词中随机和动态创建层次下拉菜单和相关用户任务来确保菜单深度和上下文的巨大变化。通过在模型培训期间合并用户的非语言标准性能WAIS-IV，可以降低主观性能偏差。菜单的语义信息是使用通用句子编码器编码的。我们介绍了一项用户研究的结果，该结果表明，提出的预测模型在预测具有各种认知能力的用户的层次菜单方面具有很高的准确性。据我们所知，这是预测CE为沉浸式AR应用设计UI时的第一项工作。
There are currently limited guidelines on designing user interfaces (UI) for immersive augmented reality (AR) applications. Designers must reflect on their experience designing UI for desktop and mobile applications and conjecture how a UI will influence AR users' performance. In this work, we introduce a predictive model for determining users' performance for a target UI without the subsequent involvement of participants in user studies. The model is trained on participants' responses to objective performance measures such as consumed endurance (CE) and pointing time (PT) using hierarchical drop-down menus. Large variability in the depth and context of the menus is ensured by randomly and dynamically creating the hierarchical drop-down menus and associated user tasks from words contained in the lexical database WordNet. Subjective performance bias is reduced by incorporating the users' non-verbal standard performance WAIS-IV during the model training. The semantic information of the menu is encoded using the Universal Sentence Encoder. We present the results of a user study that demonstrates that the proposed predictive model achieves high accuracy in predicting the CE on hierarchical menus of users with various cognitive abilities. To the best of our knowledge, this is the first work on predicting CE in designing UI for immersive AR applications.
