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Personal and Ubiquitous Computing

, Volume 18, Issue 2, pp 355–368 | Cite as

Community Similarity Networks

  • Nicholas D. Lane
  • Ye Xu
  • Hong Lu
  • Shaohan Hu
  • Tanzeem Choudhury
  • Andrew T. Campbell
  • Feng Zhao
Original Article

Abstract

Sensor-enabled smartphones are opening a new frontier in the development of mobile sensing applications. The recognition of human activities and context from sensor data using classification models underpins these emerging applications. However, conventional approaches to training classifiers struggle to cope with the diverse user populations routinely found in large-scale popular mobile applications. Differences between users (e.g., age, sex, behavioral patterns, lifestyle) confuse classifiers, which assume everyone is the same. To address this, we propose Community Similarity Networks (CSN), which incorporates inter-person similarity measurements into the classifier training process. Under CSN, every user has a unique classifier that is tuned to their own characteristics. CSN exploits crowd-sourced sensor data to personalize classifiers with data contributed from other similar users. This process is guided by similarity networks that measure different dimensions of inter-person similarity. Our experiments show CSN outperforms existing approaches to classifier training under the presence of population diversity.

Keywords

Smartphone sensing Activity recognition Community-guided learning 

References

  1. 1.
    Amazon Mechanical Turk. http://www.mturk.com
  2. 2.
  3. 3.
  4. 4.
    SF-36.org. A community for measuring health outcoming using SF tools. http://www.sf-36.org/tools/SF36.shtml
  5. 5.
    Amazon Elastic Cloud Computing. http://aws.amazon.com/ec2
  6. 6.
    Guo B, Zhang D, Yu Z, Zhou X (2012) Hybrid SN: interlinking opportunistic and online communities to augment information dissemination. In: Proceedings of the 9th IEEE international conference on ubiquitous intelligence and computing, UIC’12Google Scholar
  7. 7.
    Abdelzaher T, Anokwa Y, Boda P, Burke J, Estrin D, Guibas L, Kansal A, Madden S, Reich J (2007) Mobiscopes for human spaces. IEEE Pervasive Comput 6(2):20–29CrossRefGoogle Scholar
  8. 8.
    Andoni A, Indyk P (2008) Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions. Commun ACM 51(1):117–122CrossRefGoogle Scholar
  9. 9.
    Ashbrook D, Starner T (2003) Using GPS to learn significant locations and predict movement across multiple users. Pers Ubiquitous Comput 7(5):275–286CrossRefGoogle Scholar
  10. 10.
    Bishop CM (2006) Pattern recognition and machine learning (information science and statistics). Springer, New YorkGoogle Scholar
  11. 11.
    Burke J, Estrin D, Hansen M, Parker A, Ramanathan N, Reddy S, Srivastava MB (2006) Participatory sensing. In: Workshop on world-sensor-web: mobile device centric sensor networks and applications, WSW ’06, pp 117–134Google Scholar
  12. 12.
    Hattori Y, Inoue S, Hirakawa G. (2011) A large scale gathering system for activity data with mobile sensors. In: Proceedings of the 15th IEEE international symposium on wearable computers, (ISWC 2011)Google Scholar
  13. 13.
    Campbell AT, Eisenman SB, Lane ND, Miluzzo E, Peterson RA (2006) People-centric urban sensing. In: Proceedings of the 2nd annual international wireless internet conference, WICON ’06Google Scholar
  14. 14.
    Consolvo S, McDonald DW, Toscos T, Chen MY, Froehlich J, Harrison B, Klasnja P, LaMarca A, LeGrand L, Libby R, Smith I, Landay JA (2008) Activity sensing in the wild: a field trial of UbiFit Garden. In: Proceedings of the 26th annual SIGCHI conference on human factors in computing systems, CHI ’08, pp 1797–1806Google Scholar
  15. 15.
    Dipietro L, Caspersen C, Ostfeld A, Nadel E (1993) A survey for assessing physical activity among older adults. Med Sci Sports Exerc 25:628–628CrossRefGoogle Scholar
  16. 16.
    Froehlich J, Dillahunt T, Klasnja P, Mankoff J, Consolvo S, Harrison B, Landay JA (2009) UbiGreen: investigating a mobile tool for tracking and supporting green transportation habits. In: Proceedings of the 27th international conference on human factors in computing systems, CHI ’09, pp 1043–1052Google Scholar
  17. 17.
    Kapoor A, Horvitz E (2008) Experience sampling for building predictive user models: a comparative study. In: Proceedings of the 26th annual SIGCHI conference on human factors in computing systems, CHI ’08, pp 657–666Google Scholar
  18. 18.
    Lane ND, Xu Y, Lu H, Hu S, Choudhury T, Campbell AT, Zhao F (2011) Enabling large-scale human activity inference on smartphones using community similarity networks (CSN). In: Proceedings of the 13th international conference on ubiquitous computing, Ubicomp ’11, pp 355–364Google Scholar
  19. 19.
    Lane ND, Lu H, Eisenman SB, Campbell AT (2008) Cooperative techniques supporting sensor-based people-centric inferencing. In: Proceedings of the 6th international conference on pervasive computing, Pervasive ’08, pp 75–92Google Scholar
  20. 20.
    Lane ND, Xu Y, Lu H, Campbell AT, Choudhury T, Eisenman SB (2011) Exploiting social networks for large-scale human behavior modeling. Pervasive Comput 104:45–53CrossRefGoogle Scholar
  21. 21.
    Lane ND, Mohammod M, Lin M, Yang X, Lu H, Ali S, Doryab A, Berke E, Choudhury T, Campbell AT (2011) BeWell: a smartphone application to monitor, model and promote wellbeing. In: Proceedings of the 5th international ICST conference on pervasive computing technologies for healthcare, Pervasive Health 11Google Scholar
  22. 22.
    Lester J, Choudhury T, Kern N, Borriello G, Hannaford B (2005) A hybrid discriminative/generative approach for modeling human activities. In: Proceedings of the international joint conference on artificial intelligence, IJCAI ’05, pp 766–772Google Scholar
  23. 23.
    Longstaff B, Reddy S, Estrin D (2010) Improving activity classification for health applications on mobile devices using active and semi-supervised learning. In: Proceedings of ICST conference on pervasive computing technologies for healthcare, PervasiveHealth ’10, pp 1–7Google Scholar
  24. 24.
    Lu H, Pan W, Lane ND, Choudhury T, Campbell AT (2009) Soundsense: scalable sound sensing for people-centric applications on mobile phones. In: Proceedings of the 7th international conference on mobile systems, applications, and services, Mobisys ’09, pp 165–178Google Scholar
  25. 25.
    Lu H, Yang J, Liu Z, Lane ND, Choudhury T, Campbell AT (2010) The Jigsaw continuous sensing engine for mobile phone applications. In: Proceedings of the 8th international conference on embedded networked sensor systems, Sensys ’10, pp 71–84Google Scholar
  26. 26.
    Mun M, Reddy S, Shilton K, Yau N, Burke J, Estrin D, Hansen M, Howard E, West R, Boda P (2009) PEIR, the personal environmental impact report, as a platform for participatory sensing systems research. In: Proceedings of the 7th international conference on mobile systems, applications, and services, MobiSys ’09, pp 55–68Google Scholar
  27. 27.
    Oza NC, Russell S. (2001) Online bagging and boosting. In: Proceedings of the 8th international workshop on artificial intelligence and statistics, AISTAT ’01, pp 105–112Google Scholar
  28. 28.
    Miluzzo E, Oakley JM, Lu H, Lane ND, Peterson R, Campbell A. (2008) Evaluating the iPhone as a mobile platform for people-centric sensing applications. In: Proceedings of international workshop on urban, community, and social applications of networked sensing systems, UrbanSense 08Google Scholar
  29. 29.
    Peebles D, Lu H, Lane N, Choudhury T, Campbell A (2010) Community-guided learning: exploiting mobile sensor users to model human behavior. In: Proceedings of the 24th national conference on artificial intelligence, AAAI ’10, pp 1600–1606Google Scholar
  30. 30.
    Yan T, Kumar V, Ganesan D (2010) CrowdSearch: exploiting crowds for accurate real-time image search on mobile phones. In: Proceedings of the 8th international conference on mobile systems, applications, and services, MobiSys ’10, pp 77–90Google Scholar
  31. 31.
    Rachuri KK, Musolesi M, Mascolo C, Rentfrow PJ, Longworth C, Aucinas A (2010) Emotionsense: a mobile phones based adaptive platform for experimental social psychology research. In: Proceedings of the 12th ACM international conference on ubiquitous computing, Ubicomp ’10, pp 281–290Google Scholar
  32. 32.
    Stikic M, Van Laerhoven K, Schiele B (2008) Exploring semi-supervised and active learning for activity recognition. In: Proceedings of the 12th IEEE international symposium on wearable computers, ISWC ’08, pp 81–88Google Scholar
  33. 33.
    von Ahn L, Maurer B, Mcmillen C, Abraham D, Blum M (2008) reCAPTCHA: human-based character recognition via web security measures. Science, pp 1465–1468Google Scholar
  34. 34.
    Rubner CTY, Guibas LJ (2000) The earth movers distance as a metric for image retrieval. IJCV 40(2):99–121CrossRefzbMATHGoogle Scholar
  35. 35.
    Zheng Y, Li Q, Chen Y, Xie X, Ma WY (2008) Understanding mobility based on GPS data. In: Proceedings of the 10th ACM international conference on ubiquitous computing, Ubicomp ’08, pp 312–321Google Scholar
  36. 36.
    Zheng Y, Zhang L, Xie X, Ma WY (2009) Mining interesting locations and travel sequences from GPS trajectories. In: Proceedings of the 18th international conference on world wide web, WWW ’09, pp 791–800Google Scholar
  37. 37.
    Zhu X, Goldberg AB (2009) Introduction to semi-supervised learning. Morgan and Claypool, San RafaelzbMATHGoogle Scholar

Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  • Nicholas D. Lane
    • 1
  • Ye Xu
    • 2
  • Hong Lu
    • 3
  • Shaohan Hu
    • 4
  • Tanzeem Choudhury
    • 5
  • Andrew T. Campbell
    • 2
  • Feng Zhao
    • 1
  1. 1.Microsoft Research AsiaBeijingChina
  2. 2.Dartmouth CollegeHanoverUSA
  3. 3.Intel LabsSanta ClaraUSA
  4. 4.University of Illinois at Urbana-ChampaignUrbanaUSA
  5. 5.Cornell UniversityIthacaUSA

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