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Toward the markerless and automatic analysis of kinematic features: A toolkit for gesture and movement research

机译:进行运动特征的无标记自动分析:手势和运动研究工具包

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摘要

Action, gesture, and sign represent unique aspects of human communication that use form and movement to convey meaning. Researchers typically use manual coding of video data to characterize naturalistic, meaningful movements at various levels of description, but the availability of markerless motion-tracking technology allows for quantification of the kinematic features of gestures or any meaningful human movement. We present a novel protocol for extracting a set of kinematic features from movements recorded with Microsoft Kinect. Our protocol captures spatial and temporal features, such as height, velocity, submovements/strokes, and holds. This approach is based on studies of communicative actions and gestures and attempts to capture features that are consistently implicated as important kinematic aspects of communication. We provide open-source code for the protocol, a description of how the features are calculated, a validation of these features as quantified by our protocol versus manual coders, and a discussion of how the protocol can be applied. The protocol effectively quantifies kinematic features that are important in the production (e.g., characterizing different contexts) as well as the comprehension (e.g., used by addressees to understand intent and semantics) of manual acts. The protocol can also be integrated with qualitative analysis, allowing fast and objective demarcation of movement units, providing accurate coding even of complex movements. This can be useful to clinicians, as well as to researchers studying multimodal communication or human–robot interactions. By making this protocol available, we hope to provide a tool that can be applied to understanding meaningful movement characteristics in human communication.Electronic supplementary materialThe online version of this article (10.3758/s13428-018-1086-8) contains supplementary material, which is available to authorized users.
机译:动作,手势和符号代表人类交流的独特方面,它们使用形式和动作来传达含义。研究人员通常使用视频数据的手动编码来描述各种描述级别的自然,有意义的动作,但是无标记运动跟踪技术的可用性允许量化手势或任何有意义的人类动作的运动学特征。我们提出了一种新颖的协议,用于从用Microsoft Kinect记录的运动中提取运动学特征集。我们的协议捕获了空间和时间特征,例如高度,速度,子移动/笔划和保持。这种方法是基于对交流动作和手势的研究,并试图捕获一直被认为是交流的重要运动学方面的特征。我们为该协议提供了开源代码,描述了如何计算功能,验证了我们的协议与手动编码器相比所量化的这些功能,并讨论了如何应用该协议。该协议有效地量化了在动作的产生(例如,表征不同背景)和理解(例如,收件人用于理解意图和语义)中重要的运动学特征。该协议还可以与定性分析相集成,从而可以快速,客观地划分运动单位,甚至可以为复杂的运动提供准确的编码。这对于临床医生以及研究多模式通信或人机交互的研究人员可能很有用。通过使该协议可用,我们希望提供一种可用于理解人类交流中有意义的运动特征的工具。电子补充材料本文的在线版本(10.3758 / s13428-018-1086-8)包含补充材料,可供授权用户使用。

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