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dc.contributor.authorKatevas, Ken_US
dc.contributor.authorHänsel, Ken_US
dc.contributor.authorClegg, Ren_US
dc.contributor.authorLeontiadis, Ien_US
dc.contributor.authorHaddadi, Hen_US
dc.contributor.authorTokarchuk, Len_US
dc.date.accessioned2018-11-30T10:54:34Z
dc.date.submitted2018-11-07T12:12:06.479Z
dc.identifier.urihttp://qmro.qmul.ac.uk/xmlui/handle/123456789/53426
dc.description21 pages, 6 figures, conference paperen_US
dc.description.abstractRemembering 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.en_US
dc.subjectcs.HCen_US
dc.subjectcs.HCen_US
dc.subjectcs.LGen_US
dc.subjectstat.MLen_US
dc.titleFinding Dory in the Crowd: Detecting Social Interactions using Multi-Modal Mobile Sensingen_US
dc.typeArticle
dc.rights.holder© The Author(s) 2018
pubs.author-urlhttp://arxiv.org/abs/1809.00947v2en_US
pubs.notesNot knownen_US
qmul.funderAnalysing & Influencing Crowd Behaviour::Defence Science and Technology Laboratoryen_US


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