Detecting Group Formations using iBeacon Technology

Submitted by richard on Sun, 07/10/2016 - 16:30
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.

title={Detecting Group Formations using iBeacon Technology},
author= {Kleomenis Katevas and Hamed Haddadi and Laurissa Tokarchuk and Richard G. Clegg},
booktitle={Proc. of Human Activity Sensing Corpus and Applications Workshop}
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