具有功能应用独立性的非接触手势交互广义模型

Song Wang, Hao Hu, Hao Long, Liang Liu, Yonghui Chen, Yadong Wu
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引用次数: 0

摘要

沉浸式分析(Immersive Analytics, IA)作为一种新兴技术,已被广泛应用于流场、网络可视化、体可视化等领域。交互方式分为手柄式和非接触式手势等。在非接触手势中,领域数据的特殊属性和开发人员的主观因素导致手势动作在数据领域之间存在一定的差异。通过我们的研究,我们认为手势动作存在跨域的通用特征,包括受交互范式约束的旋转和移动手势。因此,我们提出了一种通用的非接触手势交互模型,以保证数据域的独立性;我们期望该模型能够有效地适应IA中多种类型的数据域。提出了基于微表情的手势舒适度评价模型,开发了手势动作集;引入了基于WYTIWYG (What You Touch Is What You Get)概念的手势交互设计;定义了通用的手势交互机制,防止功能依赖;实现了一种面向沉浸式环境的非接触通用手势动作范式。随后,我们招募了30名参与者对这三种情况进行评估并记录他们的微表情。最后对微表情进行分析,并进行问卷调查;将微表情评估结果与手势评估模型进一步交叉验证,以验证所提通用模型的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Generalized Model for Non-Contact Gesture Interaction with Function Application Independence
Immersive Analytics(IA), has been broadly used as an emerging technology in fields including Flow Field, Network Visualization, Volume Visualization, etc. The interaction methods are categorized into handle device and non-contact gestures, etc. In non-contact gestures, the peculiar attributes of the domain data and the subjective factors of the developers cause some variation in gesture actions across data domains. Through our investigation, we believe that there exist generic features of gesture actions across domains, including rotate and move gestures bound by interaction paradigms. Therefore, we proposed a generic model of non-contact gesture interaction to ensure data domain independence; we anticipate that the model can be efficiently adapted to multiple types of data domains in IA. We presented a gesture comfort evaluation model with micro-expressions to develop a gesture action set, introduced a gesture interaction design based on WYTIWYG (What You Touch Is What You Get) concept, defined a generic gesture interaction mechanism to prevent functional dependency, and implemented a non-contact generic gesture action paradigm for immersive environments. Subsequently, we recruited 30 participants to evaluate the three cases and recorded their micro-expressions. Finally, the micro-expressions were analyzed and a questionnaire survey was performed; the outcomes of the micro-expressions assessment were further cross-validated with the gesture evaluation model to confirm the validity of the proposed general model.
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