SOCIAL Enabling spontaneous social communication in spatially distributed groups by ambient intelligent environments

自发的和非正式的沟通对生产力，社会认同和工作群体的福祉有很大的影响。在空间配置的团队中，这种自发的沟通发生在日常的基础上：人们偶尔会在办公室的地板上，在咖啡角或午餐时见面。然而，今天我们经常遇到空间分布的工作环境，这阻碍了同事之间的随意交流，因为团队分布在位于不同城市和国家的分支办公室。同时，用于远程通信的现有方法，包括电话、电子邮件、视频会议或社交网络，大多以支持明确的、有计划的和正式的通信为目标。因此，他们通常无法适当地模仿自发的和非正式的人际沟通的复杂性。在拟议的项目中，我们的目标是重建的概念，并在空间分布的设置自发通信。我们将通过观察、分类和形式化并置工作组设置中的自发沟通情况来处理这个问题空间。作为下一步，我们将开发计算检测分布式设置中类似情况的方法。该检测将通过使用跨越分布式位置的周围智能环境识别和解释环境特征和行为线索来实现。我们将使用不引人注目和保护隐私的多模态输出向用户传达检测到的情况，并将开发适当的交互方法，使用户能够无缝地参与分布式通信。我们的工作将有助于在分布式环境中的计算机支持的通信新的见解，并将推进在普适通信，周围智能环境，空间表示和推理，多模态人机交互的艺术状态。

Spontaneous and informal communication has a strong impact on the productivity, social identity, and wellbeing of work groups. In spatially collocated teams, such spontaneous communication occurs on a day-to-day basis: People occasionally meet on office floors, at coffee corners, or at lunch. However, today we often encounter spatially distributed work settings that impede casual communication between co-workers, as teams are distributed over branch offices located in different cities and countries. At the same time, existing methods for remote communication, including telephone, e-mail, videoconferencing, or social networks, are mostly targeted at supporting explicit, planned, and formal communication. Consequently, they typically fail to appropriately mimic the complexity of spontaneous and informal interpersonal communication. In the proposed project, we aim to recreate the notion of collocated spontaneous communication in spatially distributed settings. We will approach this problem space by observing, classifying, and formalizing spontaneous communication situations in collocated work group settings. As a next step, we will develop methods to computationally detect similar situations in distributed settings. The detection will be realized by recognizing and interpreting environmental features and behavioral cues using an ambient intelligence environment spanning distributed locations. We will convey detected situations to users using unobtrusive and privacy-preserving multi-modal output, and will develop appropriate interaction methods enabling users to seamlessly engage in distributed communication. Our work will contribute novel insights on computer-supported communication in distributed environments and will advance the state of the art in Pervasive Communication, Ambient Intelligence Environments, Spatial Representation and Reasoning, and multimodal Human- Computer Interaction.