Trade-offs in monitoring social interactions

Social interaction is one of the basic components of human life that impacts thoughts, emotions, decisions, and the overall wellbeing of individuals. In this regard, monitoring social activity constitutes an important factor in a number of disciplines, particularly those related to social and health sciences. Sensor-based social interaction data collection has been seen as a groundbreaking tool, having the potential to overcome the drawbacks of traditional self-reporting methods and revolutionize social behavior analysis. However, monitoring of social interactions typically implies a trade-off between the quality of collected data and the levels of unobtrusiveness and privacy respect, aspects that can affect spontaneity in subjects' behavior. In this article we discuss the challenges of automatic monitoring of social interactions; then we provide an overview of the current automatic monitoring concepts and the associated trade-offs. We finally present our approach of using non-visual and non-auditory mobile sources that mitigate privacy concerns and do not interfere with individuals daily routines, while providing a reliable platform for social interaction data collection.

[1]  Maja Pantic,et al.  Social signal processing: Survey of an emerging domain , 2009, Image Vis. Comput..

[2]  Jie Liu,et al.  A rotation based method for detecting on-body positions of mobile devices , 2011, UbiComp '11.

[3]  P. Killworth,et al.  The Problem of Informant Accuracy: The Validity of Retrospective Data , 1984 .

[4]  David Lazer,et al.  Inferring friendship network structure by using mobile phone data , 2009, Proceedings of the National Academy of Sciences.

[5]  Alfred Kobsa,et al.  An empirical investigation of concerns of everyday tracking and recording technologies , 2008, UbiComp.

[6]  Merryn J Mathie,et al.  Accelerometry: providing an integrated, practical method for long-term, ambulatory monitoring of human movement , 2004, Physiological measurement.

[7]  Alex Pentland,et al.  Reality mining: sensing complex social systems , 2006, Personal and Ubiquitous Computing.

[8]  Oscar Mayora-Ibarra,et al.  Multi-modal mobile sensing of social interactions , 2012, 2012 6th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops.

[9]  Nathan Eagle,et al.  Machine perception and learning of complex social systems , 2005 .

[10]  John Krumm,et al.  The NearMe Wireless Proximity Server , 2004, UbiComp.

[11]  PentlandAlex,et al.  Reality mining: sensing complex social systems , 2006 .

[12]  Oscar Mayora-Ibarra,et al.  FM radio for indoor localization with spontaneous recalibration , 2010, Pervasive Mob. Comput..

[13]  Alec Wolman,et al.  Virtual Compass: Relative Positioning to Sense Mobile Social Interactions , 2010, Pervasive.

[14]  Alex Pentland,et al.  Sensing and modeling human networks , 2004 .

[15]  Jeff A. Bilmes,et al.  Inferring colocation and conversation networks from privacy-sensitive audio with implications for computational social science , 2011, TIST.

[16]  Dinesh Babu Jayagopi,et al.  Computational modeling of face-to-face social interaction using nonverbal behavioral cues , 2011, J. Ambient Intell. Smart Environ..

[17]  Oscar Mayora-Ibarra,et al.  Analysis of Social Interactions Through Mobile Phones , 2012, Mob. Networks Appl..

[18]  Guobin Shen,et al.  BeepBeep: a high accuracy acoustic ranging system using COTS mobile devices , 2007, SenSys '07.

[19]  Gerd Kortuem,et al.  A relative positioning system for co-located mobile devices , 2005, MobiSys '05.

[20]  Georg Groh,et al.  Detecting Social Situations from Interaction Geometry , 2010, 2010 IEEE Second International Conference on Social Computing.

[21]  Oscar Mayora-Ibarra,et al.  Speech activity detection using accelerometer , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.