Interpersonal distance is a core aspect of mother-child interaction. While conventional measures based on human coders do not fully capture the dynamics of this feature, computational methods provide automatic measures which can detect even small changes and more accurate estimates both spatially and temporally. Using RGB-D sensors (Microsoft Kinect V2), the present study describes a setup to automatically examine interpersonal distance during mother-child interactions, termed Mother-Infant Interaction Kinect Analysis (MIIKA). First, the laboratory setting and the data extraction method are described. By using an ad-hoc algorithm for kinematic data extraction, MIIKA returns three metrics: barycenter position (distance and velocity of approach and separation), movements (number of small, medium and large approaches and separations) and contributions (proportional contributions of mother and child to approaches and separations). Secondly, preliminary MIIKA metrics are described for a non-clinical mother-child dyad as an exemplification of the protocol. As interpersonal distance can be affected by contingent situations, we detected mother-infant full skeleton during three interactional contexts characterized by different kinds of dyadic exchanges: a free play session, a task-oriented activity and an emotionally arousing condition. Results highlighted similarities and differences between the three interactional contexts. MIIKA appears to be a promising setup to automatically examine interpersonal distance in early mother-child interactions.

人际距离是母子互动的一个核心方面。虽然基于人工编码器的传统测量不能完全捕捉此特征的动态，但计算方法提供了自动测量，可以检测甚至很小的变化，并在空间和时间上进行更准确的估计。本研究使用 RGB-D 传感器 (Microsoft Kinect V2)，描述了一种在母婴互动过程中自动检查人际距离的设置，称为母婴互动 Kinect 分析 (MIIKA)。首先，描述了实验室设置和数据提取方法。通过使用特殊算法进行运动学数据提取，MIIKA 返回三个指标：重心位置（接近和分离的距离和速度）、运动（小型、中型和大型接近和分离的数量）和贡献（母婴对接近和分离的贡献比例）。其次，描述了非临床母子二人组的初步 MIIKA 指标，作为协议的例证。由于人际距离会受到偶然情况的影响，我们在以不同类型的二元交换为特征的三种交互环境中检测到母婴完整骨架：自由游戏会话、任务导向的活动和情绪激发状态。结果突出了三种交互环境之间的异同。MIIKA 似乎是一个很有前途的设置，可以自动检查早期母子互动中的人际距离。