Connecting personal-scale sensing and networked community behavior to infer human activities

Advances in mobile and wearable devices are making it feasible to deploy sensing systems at a large-scale. However, slower progress is being made in activity recognition which remains often unreliable in everyday environments. In this paper, we investigate how to leverage the increasing capacity to gather data at a population-scale towards improving existing models of human behavior. Specifically, we consider the various social phenomena and environmental factors that cause people to develop correlated behavioral patterns, especially within communities connected by strong social ties. Reasons underpinning correlated behavior include shared externalities (e.g., work schedules, weather, traffic conditions), that shape options and decisions; and cases of adopted behavior, as people learn from each other or assume group norms due to social pressure. Most existing approaches to modeling human behavior ignore all of these phenomena and recognize activities solely on the basis of sensor data captured from a single individual. We propose the Networked Community Behavior (NCB) framework for activity recognition, specifically designed to exploit community-scale behavioral patterns. Under NCB, patterns of community behavior are mined to identify social ties that can signal correlated behavior, this information is used to augment sensor-based inferences available from the actions of individuals. Our evaluation of NCB shows it is able to outperform existing approaches to behavior modeling across four mobile sensing datasets that collectively require a diverse set of activities to be recognized.

[1]  Andrew T. Campbell,et al.  Cooperative Communities (CoCo): Exploiting Social Networks for Large-scale Modeling of Human Behavior , 2011 .

[2]  M. McPherson,et al.  Birds of a Feather: Homophily in Social Networks , 2001 .

[3]  Michael Beigl,et al.  Towards Collaborative Group Activity Recognition Using Mobile Devices , 2013, Mob. Networks Appl..

[4]  Christopher M. Bishop,et al.  Pattern Recognition and Machine Learning (Information Science and Statistics) , 2006 .

[5]  Xing Xie,et al.  Mining interesting locations and travel sequences from GPS trajectories , 2009, WWW '09.

[6]  Aniket Kittur,et al.  Bridging the gap between physical location and online social networks , 2010, UbiComp.

[7]  P. Libby.  The Scientific American , 1881, Nature.

[8]  Steven W. Zucker,et al.  On the Foundations of Relaxation Labeling Processes , 1983, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  N. Christakis,et al.  The Spread of Obesity in a Large Social Network Over 32 Years , 2007, The New England journal of medicine.

[10]  Deborah Estrin,et al.  Improving activity classification for health applications on mobile devices using active and semi-supervised learning , 2010, 2010 4th International Conference on Pervasive Computing Technologies for Healthcare.

[11]  David W. McDonald,et al.  Activity sensing in the wild: a field trial of ubifit garden , 2008, CHI.

[12]  H. Kelman.  Compliance, identification, and internalization three processes of attitude change , 1958 .

[13]  Nicu Sebe,et al.  Friends don't lie: inferring personality traits from social network structure , 2012, UbiComp.

[14]  L. da F. Costa,et al.  Characterization of complex networks: A survey of measurements , 2005, cond-mat/0505185.

[15]  M E J Newman,et al.  Community structure in social and biological networks , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[16]  Andrew T. Campbell,et al.  Bewell: A smartphone application to monitor, model and promote wellbeing , 2011, PervasiveHealth 2011.

[17]  Eliot R. Smith,et al.  Situating Social Influence Processes: Dynamic, Multidirectional Flows of Influence Within Social Networks , 2007, Personality and social psychology review : an official journal of the Society for Personality and Social Psychology, Inc.

[18]  Andrew McCallum,et al.  Introduction to Statistical Relational Learning , 2007 .

[19]  J. Russell.  A circumplex model of affect. , 1980 .

[20]  Jian Lu,et al.  Recognizing multi-user activities using wearable sensors in a smart home , 2011, Pervasive Mob. Comput..

[21]  Kalyan Moy Gupta,et al.  Cautious Collective Classification , 2009, J. Mach. Learn. Res..

[22]  Charu C. Aggarwal,et al.  Social Network Data Analytics , 2011 .

[23]  Nicholas D. Lane.  Community-Aware Smartphone Sensing Systems , 2012, IEEE Internet Computing.

[24]  Éva Tardos,et al.  Influential Nodes in a Diffusion Model for Social Networks , 2005, ICALP.

[25]  N. Christakis,et al.  SUPPLEMENTARY ONLINE MATERIAL FOR: The Collective Dynamics of Smoking in a Large Social Network , 2022 .

[26]  N. Stanietsky,et al.  The interaction of TIGIT with PVR and PVRL2 inhibits human NK cell cytotoxicity , 2009, Proceedings of the National Academy of Sciences.

[27]  G. Bell,et al.  A digital life , 2007 .

[28]  Azriel Rosenfeld,et al.  Scene Labeling by Relaxation Operations , 1976, IEEE Transactions on Systems, Man, and Cybernetics.

[29]  Philip S. Yu,et al.  Collective prediction with latent graphs , 2011, CIKM '11.

[30]  Christos Faloutsos,et al.  It's who you know: graph mining using recursive structural features , 2011, KDD.

[31]  Jian Lu,et al.  Recognizing Multiuser Activities Using Wireless Body Sensor Networks , 2011, IEEE Transactions on Mobile Computing.

[32]  Alex Pentland,et al.  Pervasive Sensing to Model Political Opinions in Face-to-Face Networks , 2011, Pervasive.

[33]  Ye Xu,et al.  Enabling large-scale human activity inference on smartphones using community similarity networks (csn) , 2011, UbiComp '11.

[34]  Dino Pedreschi,et al.  Human mobility, social ties, and link prediction , 2011, KDD.

[35]  Alex Pentland,et al.  Sensible Organizations: Technology and Methodology for Automatically Measuring Organizational Behavior , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[36]  Ye Xu,et al.  Exploiting Social Networks for Large-Scale Human Behavior Modeling , 2011, IEEE Pervasive Computing.

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

[38]  Jimeng Sun,et al.  A Survey of Models and Algorithms for Social Influence Analysis , 2011, Social Network Data Analytics.

[39]  Nasser M. Nasrabadi,et al.  Pattern Recognition and Machine Learning , 2006, Technometrics.

[40]  Yunxin Liu,et al.  Can Your Smartphone Infer Your Mood ? , 2011 .

[41]  Ben Taskar,et al.  Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning) , 2007 .