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Author Palaghias, Niklas ♦ Hoseini-Tabatabaei, Amir ♦ Nati, Michele ♦ Gluhak, Alexander ♦ Moessner, Klaus
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Copyright Year ©2016
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Social signal processing ♦ Mobile phones ♦ Social behavior
Abstract Understanding human behavior in an automatic but nonintrusive manner is an important area for various applications. This requires the collaboration of information technology with human sciences to transfer existing knowledge of human behavior into self-acting tools. These tools will reduce human error that is introduced by current obtrusive methods such as questionnaires. To achieve unobtrusiveness, we focus on exploiting the pervasive and ubiquitous character of mobile devices. In this article, a survey of existing techniques for extracting social behavior through mobile devices is provided. Initially, we expose the terminology used in the area and introduce a concrete architecture for social signal processing applications on mobile phones, constituted by $\textit{sensing},$ social interaction detection, behavioral cues extraction, social signal inference, and social behavior understanding. Furthermore, we present state-of-the-art techniques applied to each stage of the process. Finally, potential applications are shown while arguing about the main challenges of the area.
ISSN 03600300
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2016-03-01
Publisher Place New York
e-ISSN 15577341
Journal ACM Computing Surveys (CSUR)
Volume Number 48
Issue Number 4
Page Count 52
Starting Page 1
Ending Page 52


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Source: ACM Digital Library