Paper Info
Title
Detecting group formations using iBeacon technology.
Abstract
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 Apple's iBeacon™ 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.
Year
DOI
Venue
2016
10.1145/2968219.2968281
UbiComp '16: The 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing Heidelberg Germany September, 2016
Keywords
Field
DocType
Mobile Sensing, Crowd Sensing, BLE, iBeacon, RSSI, Group Formations, Social Interactions, Social Network Analysis
Graph theory,Crowds,Computer science,iBeacon,Ground truth,Human–computer interaction,Analytics,Bluetooth,Crowd psychology,Control reconfiguration
Conference
ISBN
Citations 
PageRank 
978-1-4503-4462-3
3
0.57
References 
Authors
9
4
Name
Order
Citations
PageRank
Kleomenis Katevas1395.89
Hamed Haddadi222322.94
Laurissa Tokarchuk3285.34
Richard G. Clegg423729.78