Jang Sujin, Elmqvist Niklas, Ramani Karthik
IEEE Trans Vis Comput Graph. 2016 Jan;22(1):21-30. doi: 10.1109/TVCG.2015.2468292.
Pattern analysis of human motions, which is useful in many research areas, requires understanding and comparison of different styles of motion patterns. However, working with human motion tracking data to support such analysis poses great challenges. In this paper, we propose MotionFlow, a visual analytics system that provides an effective overview of various motion patterns based on an interactive flow visualization. This visualization formulates a motion sequence as transitions between static poses, and aggregates these sequences into a tree diagram to construct a set of motion patterns. The system also allows the users to directly reflect the context of data and their perception of pose similarities in generating representative pose states. We provide local and global controls over the partition-based clustering process. To support the users in organizing unstructured motion data into pattern groups, we designed a set of interactions that enables searching for similar motion sequences from the data, detailed exploration of data subsets, and creating and modifying the group of motion patterns. To evaluate the usability of MotionFlow, we conducted a user study with six researchers with expertise in gesture-based interaction design. They used MotionFlow to explore and organize unstructured motion tracking data. Results show that the researchers were able to easily learn how to use MotionFlow, and the system effectively supported their pattern analysis activities, including leveraging their perception and domain knowledge.
人体运动模式分析在许多研究领域都很有用，它需要理解和比较不同风格的运动模式。然而，处理人体运动跟踪数据以支持此类分析带来了巨大挑战。在本文中，我们提出了MotionFlow，这是一个视觉分析系统，它基于交互式流可视化提供各种运动模式的有效概述。这种可视化将运动序列表述为静态姿势之间的转换，并将这些序列聚合为树形图以构建一组运动模式。该系统还允许用户在生成代表性姿势状态时直接反映数据的上下文及其对姿势相似性的感知。我们对基于分区的聚类过程提供局部和全局控制。为了支持用户将非结构化运动数据组织成模式组，我们设计了一组交互，能够从数据中搜索相似的运动序列、详细探索数据子集以及创建和修改运动模式组。为了评估MotionFlow的可用性，我们对六位具有基于手势的交互设计专业知识的研究人员进行了用户研究。他们使用MotionFlow来探索和组织非结构化运动跟踪数据。结果表明，研究人员能够轻松学会如何使用MotionFlow，并且该系统有效地支持了他们的模式分析活动，包括利用他们的感知和领域知识。
