  Remembering our day-to-day social interactions is challenging even if you
aren't a blue memory challenged fish. The ability to automatically detect and
remember these types of interactions is not only beneficial for individuals
interested in their behavior in crowded situations, but also of interest to
those who analyze crowd behavior. Currently, detecting social interactions is
often performed using a variety of methods including ethnographic studies,
computer vision techniques and manual annotation-based data analysis. However,
mobile phones offer easier means for data collection that is easy to analyze
and can preserve the user's privacy. In this work, we present a system for
detecting stationary social interactions inside crowds, leveraging multi-modal
mobile sensing data such as Bluetooth Smart (BLE), accelerometer and gyroscope.
To inform the development of such system, we conducted a study with 24
participants, where we asked them to socialize with each other for 45 minutes.
We built a machine learning system based on gradient-boosted trees that
predicts both 1:1 and group interactions with 77.8% precision and 86.5% recall,
a 30.2% performance increase compared to a proximity-based approach. By
utilizing a community detection-based method, we further detected the various
group formation that exist within the crowd. Using mobile phone sensors already
carried by the majority of people in a crowd makes our approach particularly
well suited to real-life analysis of crowd behavior and influence strategies.
