Department of Human Media Interaction, University of Twente, Enschede, The Netherlands,
Behav Res Methods. 2014 Sep;46(3):625-33. doi: 10.3758/s13428-013-0398-y.
Technologies that measure human nonverbal behavior have existed for some time, and their use in the analysis of social behavior has become more popular following the development of sensor technologies that record full-body movement. However, a standardized methodology to efficiently represent and analyze full-body motion is absent. In this article, we present automated measurement and analysis of body motion (AMAB), a methodology for examining individual and interpersonal nonverbal behavior from the output of full-body motion tracking systems. We address the recording, screening, and normalization of the data, providing methods for standardizing the data across recording condition and across subject body sizes. We then propose a series of dependent measures to operationalize common research questions in psychological research. We present practical examples from several application areas to demonstrate the efficacy of our proposed method for full-body measurements and comparisons across time, space, body parts, and subjects.
测量人类非言语行为的技术已经存在了一段时间,随着能够记录全身运动的传感器技术的发展,这些技术在社会行为分析中的应用变得越来越流行。然而,目前还没有一种标准化的方法来有效地表示和分析全身运动。在本文中,我们提出了自动测量和分析身体运动(AMAB),这是一种从全身运动跟踪系统的输出中检查个体和人际非言语行为的方法。我们解决了数据的记录、筛选和规范化问题,提供了在记录条件和主体身体大小方面对数据进行标准化的方法。然后,我们提出了一系列相关的测量方法,以实现心理研究中常见研究问题的操作化。我们从几个应用领域提供了实际示例,以证明我们提出的用于全身测量以及跨时间、空间、身体部位和主体进行比较的方法的有效性。