Detecting Group Formations using iBeacon Technology

Paper type: 
Workshop paper
Kleomenis Katevas, Hamed Haddadi, Laurissa Tokarchuk, Richard G. Clegg
Human Activity Sensing Corpus and Applications Workshop
Researchers have examined crowd behavior in the past by employing a variety of methods including ethnographic studies, computer vision techniques and manual annotation based data analysis. However, because of the resources to collect, process and analyze data, it remains difficult to obtain large data sets for study. In an attempt to alleviate this problem, researchers have recently used mobile sensing, however this technique is currently only able to detect either stationary or moving crowds with questionable accuracy. In this work we present a system for detecting stationary interactions inside crowds using the Received Signal Strength Indicator of Bluetooth Smart (BLE) sensor, combined with the Motion Activity of each device. By utilizing Appleā€™s iBeacon (TM) implementation of Bluetooth Smart, we are able to detect the proximity of users carrying a smartphone in their pocket. We then use an algorithm based on graph theory to predict interactions inside the crowd and verify our findings using video footage as ground truth. Our approach is particularly beneficial to the design and implementation of crowd behavior analytics, design of influence strategies, and algorithms for crowd reconfiguration.
This paper looks at how sensor measurements in mobile phones can be used to determine when people are talking in a group.
@article{ibeacon_2016, title={Detecting Group Formations using iBeacon Technology}, author= {Kleomenis Katevas and Hamed Haddadi and Laurissa Tokarchuk and Richard G. Clegg}, year=2016, booktitle={Proc. of Human Activity Sensing Corpus and Applications Workshop} }
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