This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Exploiting Social Networks for Large-Scale Human Behavior Modeling
October-December 2011 (vol. 10 no. 4)
pp. 45-53
Nicholas D. Lane, Microsoft Research Asia
Ye Xu, Dartmouth College
Hong Lu, Dartmouth College
Andrew T. Campbell, Dartmouth College
Tanzeem Choudhury, Cornell University
Shane B. Eisenman, Harris Corporation

The Cooperative Communities (CoCo) learning framework leverages everyday social connections between people to personalize classification models. By exploiting social networks, CoCo spreads the burden of providing training data over an entire community.

1. N.D. Lane et al., "A Survey of Mobile Phone Sensing," IEEE Comm., vol. 48, no. 9, 2010, pp. 140–150.
2. S. Consolvo et al., "Activity Sensing in the Wild: A Field Trial of Ubifit Garden," Proc. Conf. Human Factors in Computing Systems (CHI 08), ACM Press, 2008, pp. 1797–1806.
3. H. Lu et al., "The Jigsaw Continuous Sensing Engine for Mobile Phone Applications," Proc. Embedded Networked Sensor Systems (SenSys 10), ACM Press, 2010, pp. 71–84.
4. Y. Zheng et al., "Understanding Mobility Based on GPS Data," Proc. Conf. Ubiquitous Computing (Ubicomp 08), ACM Press, 2008, pp. 312–321.
5. M. Mun et al., "PEIR, the Personal Environmental Impact Report, as a Platform for Participatory Sensing Systems Research," Proc. Conf. Mobile Systems, Applications, and Services (MobiSys 09), ACM Press, 2009, pp. 55–68.
6. B. Longstaff, S. Reddy, and D. Estrin, "Improving Activity Classification for Health Applications on Mobile Devices Using Active and Semi-Supervised Learning," Proc. Pervasive Health, IEEE Press, 2010, pp. 1–7.
7. H. Lu et al., "SoundSense: Scalable Sound Sensing for People-Centric Applications on Mobile Phones," Proc. Conf. Mobile Systems, Applications, and Services (MobiSys 09), ACM Press, 2009, pp. 165–178.
8. M. Stikic, K. Laerhoven, and B. Schiele, "Exploring Semi-Supervised and Active Learning for Activity Recognition," Proc. 2008 Int'l Symp. Wearable Computers, (ISWC 08), IEEE CS Press, 2008, pp. 81–88.
9. D. Peebles et al., "Community-Guided Learning: Exploiting Mobile Sensor Users to Model Human Behavior," Proc. Conf. Artificial Intelligence (AAAI 10), AAAI Press, 2010, pp. 1600–1606.
10. E. Daly and M. Haahr, "Social Network Analysis for Routing in Disconnected Delay-Tolerant MANETs," Proc. Mobile Ad Hoc Networking and Computing (MobiHoc 07), ACM Press, 2007, pp. 32–40.
11. N.A. Christakis and J. H. Fowler, "The Spread of Obesity in a Large Social Network Over 32 Years," New England J. Medicine, vol. 357, no. 4, 2007, pp. 370–379.
12. N. Eagle, A. Pentland, and D. Lazer, "Inferring Social Network Structure Using Mobile Phone Data," Proc. Nat'l Academy of Sciences (PNAS), vol. 106, no. 36, 2009, pp. 15274–15278.
13. J. Cranshaw et al., "Bridging the Gap between Physical Location and Online Social Networks," Proc. Conf. Ubiquitous Computing (Ubicomp 10), ACM Press, 2010, pp. 119–128.
14. D. Ashbrook and T. Starner, "Using GPS to Learn Significant Locations and Predict Movement across Multiple Users," Personal Ubiquitous Computing, vol. 7, no. 5, 2003, pp. 275–286.
15. W. Dong et al., "Efficiently Matching Sets of Features with Random Histograms," Proc. Conf. Multimedia (SIGMM 08), ACM Press, 2008, pp. 179–188.
16. N.D. Lane et al., "Cooperative Techniques Supporting Sensor-Based People-Centric Inferencing," Proc. Conf. Pervasive Computing, Springer, 2008, pp. 75–92.
17. N.D. Lane, "Community-Guided Mobile Phone Sensing Systems," PhD thesis, Dartmouth College, 2011.

Index Terms:
community-guided learning, mobile sensing, personalization, activity recognition, social networks
Citation:
Nicholas D. Lane, Ye Xu, Hong Lu, Andrew T. Campbell, Tanzeem Choudhury, Shane B. Eisenman, "Exploiting Social Networks for Large-Scale Human Behavior Modeling," IEEE Pervasive Computing, vol. 10, no. 4, pp. 45-53, Oct.-Dec. 2011, doi:10.1109/MPRV.2011.70
Usage of this product signifies your acceptance of the Terms of Use.