The Sociometer: A Wearable Device for Understanding Human Networks
The Sociometer: A Wearable Device for Understanding Human Networks
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Sociometer：用于理解人类网络的可穿戴设备
DOI：
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发表时间：
2002
期刊：
影响因子：
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通讯作者：
Tanzeem Choudhury
中科院分区：
文献类型：
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作者：
Tanzeem Choudhury
在本文中，我们描述了使用的社交，可穿戴传感器包，测量人与人之间的面对面的互动。我们开发了学习人类通信网络的结构和动态的方法。人们如何互动的知识在许多学科中都很重要，例如组织行为学，社会网络分析和知识管理应用，如专家发现。目前，研究者主要依靠问卷、调查或日记来获得关于人与人之间身体互动的数据。在本文中，我们将展示如何嘈杂的传感器测量的社会计量器可以用来建立计算模型的群体相互作用。使用统计模式识别技术，如动态贝叶斯网络模型，我们可以自动学习网络的底层结构，并分析个人和群体相互作用的动态。我们提出了初步的结果，我们可以学习的结构，一个组内的面对面的互动，检测当成员是面对面的接近，也当他们有一个对话。我们还衡量人与人之间互动的持续时间和频率以及每个人在对话中的参与程度。
In this paper, we describe the use of the sociometer, a wearable sensor package, for measuring face-to-face interactions between people. We develop methods for learning the structure and dynamics of human communication networks. Knowledge of how people interact is important in many disciplines, e.g. organizational behavior, social network analysis and knowledge management applications such as expert finding. At present researchers mainly have to rely on questionnaires, surveys or diaries in order to obtain data on physical interactions between people. In this paper, we show how noisy sensor measurements from the sociometer can be used to build computational models of group interactions. Using statistical pattern recognition techniques such as dynamic Bayesian network models we can automatically learn the underlying structure of the network and also analyze the dynamics of individual and group interactions. We present preliminary results on how we can learn the structure of face-to-face interactions within a group, detect when members are in face-to-face proximity and also when they are having a conversation. We also measure the duration and frequency of interactions between people and the participation level of each individual in a conversation.