标题： GIST：混合现实中的群体交互感知工具包 显示英文标题

标题： GIST: Group Interaction Sensing Toolkit for Mixed Reality

摘要： 理解团队在沉浸式环境中如何协调、共享工作和协商角色对于设计支持实时协作的有效混合现实（MR）应用至关重要。 然而，现有方法要么依赖外部摄像头和离线标注，要么仅专注于单一模态，限制了其有效性和适用性。 为了解决这个问题，我们提出了一种新颖的群体交互感知工具包（GIST），这是一个可部署的系统，能够被动地捕获多模态交互数据，例如来自商品MR头显传感器的语音、注视和空间接近度，并自动推导出整体静态交互网络和动态逐时刻行为模式。 我们通过一项涉及48名参与者、12个四人小组在MR中执行开放式图像分类任务的人类受试者研究来评估GIST。我们的分析表明，识别出的行为模式与交互网络结构的变化之间存在强烈的一致性，证实了通过传感器数据可以观察到语音、注视和接近度数据的短暂变化。 显示英文摘要

摘要： Understanding how teams coordinate, share work, and negotiate roles in immersive environments is critical for designing effective mixed-reality (MR) applications that support real-time collaboration. However, existing methods either rely on external cameras and offline annotation or focus narrowly on single modalities, limiting their validity and applicability. To address this, we present a novel group interaction sensing toolkit (GIST), a deployable system that passively captures multi-modal interaction data, such as speech, gaze, and spatial proximity from commodity MR headset's sensors and automatically derives both overall static interaction networks and dynamic moment-by-moment behavior patterns. We evaluate GIST with a human subject study with 48 participants across 12 four-person groups performing an open-ended image-sorting task in MR. Our analysis shows strong alignment between the identified behavior modes and shifts in interaction network structure, confirming that momentary changes in speech, gaze, and proximity data are observable through the sensor data.