Abstract

We propose a social network data collection method that uses wearable social sensors to automatically detect social interactions. This method offers clear advantages over traditional methods since data is automatically collected by electronic sensors rather than humans. We present the design, implementation and deployment of a wearable social sensing platform that can measure and analyze human behavior in a variety of settings and applications. Social sensors are capable of capturing individual and collective patterns of behavior by automatically measuring the amount of face-to-face interaction, conversational dynamics, physical proximity to other people, and physical activity levels. We describe five studies that have been carried out using this platform and discuss other possible application scenarios.

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