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Influence of Hand Tracking as a Way of Interaction in Virtual Reality on User Experience

机译:作为虚拟现实交互方式的手部追踪对用户体验的影响

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摘要

With the rising interest in Virtual Reality and the fast development and improvement of available devices, new features of interactions are becoming available. One of them that is becoming very popular is hand tracking, as the idea to replace controllers for interactions in virtual worlds. This experiment aims to compare different interaction types in VR using either controllers or hand tracking. Participants had to play two simple VR games with various types of tasks in those games — grabbing objects or typing numbers. While playing, they were using interactions with different visualizations of hands and controllers. The focus of this study was to investigate user experience of varying interactions (controller vs. hand tracking) for those two simple tasks. Results show that different interaction types statistically significantly influence reported emotions with Self-Assessment Manikin (SAM), where for hand tracking participants were feeling higher valence, but lower arousal and dominance. Additionally, task type of grabbing was reported to be more realistic, and participants experienced a higher presence. Surprisingly, participants rated the interaction type with controllers where both where hands and controllers were visualized as statistically most preferred. Finally, hand tracking for both tasks was rated with the System Usability Scale (SUS) scale, and hand tracking for the task typing was rated as statistically significantly more usable. These results can drive further research and, in the long term, contribute to help selecting the most matching interaction modality for a task.
机译:随着人们对虚拟现实的浓厚兴趣以及可用设备的快速开发和改进,交互的新功能也变得可用。手跟踪是一种非常流行的方法,它是替换控制器以在虚拟世界中进行交互的想法。该实验旨在比较使用控制器或手部跟踪在VR中的不同交互类型。参与者必须玩两个简单的VR游戏,这些游戏中有各种类型的任务-抓物体或键入数字。演奏时,他们正在使用具有不同手和控制器可视化效果的交互。这项研究的重点是调查针对这两个简单任务的各种交互(控制器与手部跟踪)的用户体验。结果表明,不同的互动类型在统计学上显着影响自我评估人体模型(SAM)的报告情绪,对于手部追踪参与者,其感觉价更高,但觉醒和主导力更低。此外,据报道,抢夺的任务类型更为现实,参与者的参与感也更高。出乎意料的是,参与者对与控制器的交互类型进行了评分,在统计学上,手和控制器都可视化。最后,使用系统可用性量表(SUS)量表对这两个任务的手部跟踪进行了评分,并且对任务类型的手部跟踪进行了统计上的显着提高。这些结果可以推动进一步的研究,并且从长远来看,有助于为任务选择最匹配的交互方式。

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