Paper Info
Title
Demo: Detecting Group Formations using iBeacon Technology.
Abstract
Researchers from different disciplines 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 inherent difficulties in collecting, processing and analyzing the data, it is difficult to obtain large data sets for study. In this work we present a system for detecting stationary interactions inside crowds, depending entirely on the sensors available in a modern smartphone device such as Bluetooth Smart (BLE) and Accelerometer. By utilizing Apple's iBeaconTM implementation of Bluetooth Smart using SensingKit1, our open-source multi-platform mobile sensing framework [1], 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 group interactions inside the crowd. Previous work in this area has been limited to the detection of interactions between only two people and therefore our approach goes beyond current state of the art in its ability to detect group formations with more than two people involved. Our approach is particularly beneficial to the design and implementation of crowd behavior analytics, design of influence strategies, and algorithms for crowd reconfiguration.
Year
DOI
Venue
2017
10.1145/3081333.3089335
MobiSys
Field
DocType
Citations 
Graph theory,Crowds,Computer science,Computer security,Social network analysis,iBeacon,Real-time computing,Human–computer interaction,Analytics,Bluetooth,Control reconfiguration,Crowd psychology
Conference
0
PageRank 
References 
Authors
0.34
2
5
Name
Order
Citations
PageRank
Kleomenis Katevas1395.89
Laurissa Tokarchuk2285.34
Hamed Haddadi322322.94
Richard G. Clegg423729.78
Muhammad-Naeem Irfan56829.98