As VR devices become more prevalent in the consumer space, VR applications are likely to be increasingly used by users unfamiliar with VR. Detecting the familiarity level of a user with VR as an interaction medium provides the potential of providing on-demand training for acclimatization and prevents the user from being burdened by the VR environment in accomplishing their tasks. In this work, we present preliminary results of using deep classifiers to conduct automatic detection of familiarity with VR by using hand tracking of the user as they interact with a numeric passcode entry panel to unlock a VR door. We use a VR door as we envision it to the first point of entry to collaborative virtual spaces, such as meeting rooms, offices, or clinics. Users who are unfamiliar with VR will have used their hands to open doors with passcode entry panels in the real world. Thus, while the user may not be familiar with VR, they would be familiar with the task of opening the door. Using a pilot dataset consisting of 7 users familiar with VR, and 7 not familiar with VR, we acquire highest accuracy of 88.03\% when 6 test users, 3 familiar and 3 not familiar, are evaluated with classifiers trained using data from the remaining 8 users. Our results indicate potential for using user movement data to detect familiarity for the simple yet important task of secure passcode-based access.

翻译：随着VR设备在消费领域日益普及，不熟悉VR的用户可能会越来越多地使用VR应用。检测用户对VR交互媒介的熟悉程度，能为用户提供适应性的按需培训，防止用户因VR环境在完成任务时感到负担。本研究初步展示了利用深度分类器，通过追踪用户与数字密码输入面板交互时的手部动作，来自动检测用户对VR熟悉程度的方法。我们选择VR门作为场景，因为它被设想为协作虚拟空间（如会议室、办公室或诊所）的首个入口点。不熟悉VR的用户在现实世界中曾用手操作过带密码输入面板的门，因此，尽管他们可能不熟悉VR，但他们对开门任务本身是熟悉的。通过一个包含7名熟悉VR用户和7名不熟悉VR用户的试点数据集，在6名测试用户（3名熟悉，3名不熟悉）中，使用其余8名用户的数据训练的评估器，我们取得了最高88.03%的准确率。结果表明，利用用户运动数据来检测熟悉程度，在基于密码的安全访问这一简单但重要的任务中具有潜力。