Advertisement

Human Behavior Understanding for Inducing Behavioral Change: Application Perspectives

  • Albert Ali Salah
  • Bruno Lepri
  • Fabio Pianesi
  • Alex Sandy Pentland
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7065)

Abstract

Pervasive sensing and human behavior understanding can help us in implementing or improving systems that can induce behavioral change. In this introductory paper of the 2nd International Workshop on Human Behavior Understanding (HBU’11), which has a special focus theme of “Inducing Behavioral Change”, we provide a taxonomy to describe where and how HBU technology can be harnessed to this end, and supply a short survey of the area from an application perspective. We also consider how social signals and settings relate to this concept.

Keywords

Mobile Phone Human Behavior Ubiquitous Computing Gesture Recognition Latent Dirichlet Allocation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Alemdar, H., Ersoy, C.: Wireless sensor networks for healthcare: A survey. Computer Networks 54(15), 2688–2710 (2010)CrossRefGoogle Scholar
  2. 2.
    Anderson, C.A., Bushman, B.J.: Effects of violent video games on aggressive behavior, aggressive cognition, aggressive affect, physiological arousal, and prosocial behavior: A meta-analytic review of the scientific literature. Psychological Science 12(5), 353–359 (2001)CrossRefGoogle Scholar
  3. 3.
    Avci, A., Bosch, S., Marin-Perianu, M., Marin-Perianu, R., Havinga, P.: Activity recognition using inertial sensing for healthcare, wellbeing and sports applications: A survey. In: Proc. 23rd Int. Conf. on Architecture of Computing Systems (ARCS), pp. 167–176. VDE Verlag (2010)Google Scholar
  4. 4.
    Baccouche, M., Mamalet, F., Wolf, C., Garcia, C., Baskurt, A.: Sequential Deep Learning for Human Action Recognition. In: Salah, A.A., Lepri, B. (eds.) HBU 2011. LNCS, vol. 7065, pp. 29–39. Springer, Heidelberg (2011)Google Scholar
  5. 5.
    Benevenuto, F., Rodrigues, T., Cha, M., Almeida, V.: Characterizing user behavior in online social networks. In: Proceedings of the 9th ACM SIGCOMM Conference on Internet Measurement Conference, pp. 49–62. ACM (2009)Google Scholar
  6. 6.
    Blei, D., Ng, A., Jordan, M.: Latent Dirichlet allocation. The Journal of Machine Learning Research 3, 993–1022 (2003)zbMATHGoogle Scholar
  7. 7.
    Bogost, I.: Persuasive games: The expressive power of videogames. The MIT Press (2007)Google Scholar
  8. 8.
    Boyle, E., Connolly, T.M., Hainey, T.: The role of psychology in understanding the impact of computer games. Entertainment Computing 2(2), 69–74 (2011)CrossRefGoogle Scholar
  9. 9.
    Charles, J., Everingham, M.: Learning shape models for monocular human pose estimation from the Microsoft Xbox Kinect. In: Proc. IEEE Workshop on Consumer Depth Cameras for Computer Vision (2011)Google Scholar
  10. 10.
    Chen, C.-W., Aztiria, A., Ben Allouchinst, S., Aghajan, H.: Understanding the Influence of Social Interactions on Individual’s Behavior Pattern in a Work Environment. In: Salah, A.A., Lepri, B. (eds.) HBU 2011. LNCS, vol. 7065, pp. 149–160. Springer, Heidelberg (2011)Google Scholar
  11. 11.
    Consolvo, S., Everitt, K., Smith, I., Landay, J.: Design requirements for technologies that encourage physical activity. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 457–466. ACM (2006)Google Scholar
  12. 12.
    Dura-Bernal, S., Garreau, G., Andreou, C., Andreou, A., Georgiou, J., Wennekers, T., Denham, S.: Human Action Categorization Using Ultrasound Micro-Doppler Signatures. In: Salah, A.A., Lepri, B. (eds.) HBU 2011. LNCS, vol. 7065, pp. 18–28. Springer, Heidelberg (2011)Google Scholar
  13. 13.
    Eagle, N., Pentland, A.: Reality mining: sensing complex social systems. Personal and Ubiquitous Computing 10(4), 255–268 (2006)CrossRefGoogle Scholar
  14. 14.
    Eagle, N., Pentland, A.S., Lazer, D.: Inferring friendship network structure by using mobile phone data. Proceedings of the National Academy of Sciences 106(36), 15274–15278 (2009)CrossRefGoogle Scholar
  15. 15.
    Fogg, B.J.: Persuasive technologies. Communications of the ACM 42(5), 27–29 (1999)CrossRefGoogle Scholar
  16. 16.
    Fogg, B.J.: The behavior grid: 35 ways behavior can change. In: Proceedings of the 4th International Conference on Persuasive Technology, pp. 42–46. ACM (2009)Google Scholar
  17. 17.
    Gasser, R., Brodbeck, D., Degen, M., Luthiger, J., Wyss, R., Reichlin, S.: Persuasiveness of a Mobile Lifestyle Coaching Application Using Social Facilitation. In: IJsselsteijn, W.A., de Kort, Y.A.W., Midden, C., Eggen, B., van den Hoven, E. (eds.) PERSUASIVE 2006. LNCS, vol. 3962, pp. 27–38. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  18. 18.
    Gupta, M., Intille, S., Larson, K.: Adding GPS-control to traditional thermostats: An exploration of potential energy savings and design challenges. Pervasive Computing, 95–114 (2009)Google Scholar
  19. 19.
    Hadid, A.: Analyzing Facial Behavioral Features from Videos. In: Salah, A.A., Lepri, B. (eds.) HBU 2011. LNCS, vol. 7065, pp. 52–61. Springer, Heidelberg (2011)Google Scholar
  20. 20.
    Hjelm, S.I., Browall, C.: Brainball–using brain activity for cool competition. In: Proceedings of NordiCHI, pp. 177–188 (2000)Google Scholar
  21. 21.
    Holt, B., Bowden, R.: Putting the pieces together: Connected poselets for human pose estimation. In: Proc. IEEE Workshop on Consumer Depth Cameras for Computer Vision (2011)Google Scholar
  22. 22.
    Ijsselsteijn, W.: Augmenting Social Interactions: Experiments in Socio-Emotional Computing. In: Salah, A.A., Lepri, B. (eds.) HBU 2011. LNCS, vol. 7065, p. 83. Springer, Heidelberg (2011)Google Scholar
  23. 23.
    Iso-Ketola, P., Karinsalo, T., Vanhala, J.: Hipguard: A wearable measurement system for patients recovering from a hip operation. In: Second International Conference on Pervasive Computing Technologies for Healthcare, PervasiveHealth 2008, pp. 196–199. IEEE (2008)Google Scholar
  24. 24.
    Jentsch, M., Jahn, M., Pramudianto, F., Simon, J., Al-Akkad, A.: An Energy-Saving Support System for Office Environments. In: Salah, A.A., Lepri, B. (eds.) HBU 2011. LNCS, vol. 7065, pp. 84–93. Springer, Heidelberg (2011)Google Scholar
  25. 25.
    Kalimeri, K., Lepri, B., Kim, T., Pianesi, F., Pentland, A.: Automatic Modeling of Dominance Effects Using Granger Causality. In: Salah, A.A., Lepri, B. (eds.) HBU 2011. LNCS, vol. 7065, pp. 127–136. Springer, Heidelberg (2011)Google Scholar
  26. 26.
    Kaptein, M.C., Markopoulos, P., de Ruyter, B., Aarts, E.: Persuasion in ambient intelligence. Journal of Ambient Intelligence and Humanized Computing 1(1), 43–56 (2010)CrossRefGoogle Scholar
  27. 27.
    Kempe, D., Kleinberg, J., Tardos, É.: Maximizing the spread of influence through a social network. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 137–146. ACM (2003)Google Scholar
  28. 28.
    Keskin, C., Cemgil, A.T., Akarun, L.: DTW Based Clustering to Improve Hand Gesture Recognition. In: Salah, A.A., Lepri, B. (eds.) HBU 2011. LNCS, vol. 7065, pp. 72–82. Springer, Heidelberg (2011)Google Scholar
  29. 29.
    Keskin, C., Kıraç, F., Kara, Y.E., Akarun, L.: Real time hand pose estimation using depth sensors. In: Proc. IEEE Workshop on Consumer Depth Cameras for Computer Vision (2011)Google Scholar
  30. 30.
    Kim, T., Olguín, D.O., Waber, B.N., Pentland, A.: Sensor-based feedback systems in organizational computing. In: International Conference on Computational Science and Engineering, CSE 2009, vol. 4, pp. 966–969. IEEE (2009)Google Scholar
  31. 31.
    Klein, M., Mogles, N., van Wissen, A.: Why Won’t You Do What’s Good For You? Using Intelligent Support for Behavior Change. In: Salah, A.A., Lepri, B. (eds.) HBU 2011. LNCS, vol. 7065, pp. 105–116. Springer, Heidelberg (2011)Google Scholar
  32. 32.
    Lane, N.D., Miluzzo, E., Lu, H., Peebles, D., Choudhury, T., Campbell, A.T.: A survey of mobile phone sensing. Comm. Mag. 48, 140–150 (2010)CrossRefGoogle Scholar
  33. 33.
    Lee, J., Chao, C., Thomaz, A., Bobick, A.: Adaptive Integration of Multiple Cues for Contingency Detection. In: Salah, A.A., Lepri, B. (eds.) HBU 2011. LNCS, vol. 7065, pp. 62–71. Springer, Heidelberg (2011)Google Scholar
  34. 34.
    Lepri, B., Salah, A.A., Pianesi, F., Pentland, A.: Human Behavior Understanding for Inducing Behavioral Change: Social and Theoretical Aspects. In: Wichert, R., Van Laerhoven, K., Gelissen, J. (eds.) Constructing Ambient Intelligence: AmI 2011 Workshops (2011)Google Scholar
  35. 35.
    Madan, A., Cebrian, M., Lazer, D., Pentland, A.: Social sensing for epidemiological behavior change. In: Proceedings of the 12th ACM International Conference on Ubiquitous Computing, Ubicomp 2010, pp. 291–300. ACM, New York (2010)Google Scholar
  36. 36.
    Madan, A., Farrahi, K., Gatica-Perez, D., Pentland, A.: Pervasive Sensing to Model Political Opinions in Face-to-Face Networks. In: Lyons, K., Hightower, J., Huang, E.M. (eds.) Pervasive 2011. LNCS, vol. 6696, pp. 214–231. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  37. 37.
    Madan, A., Moturu, S.T., Lazer, D., Pentland, A.S.: Social sensing: obesity, unhealthy eating and exercise in face-to-face networks. In: Wireless Health 2010, pp. 104–110. ACM, New York (2010)CrossRefGoogle Scholar
  38. 38.
    Malone, T.W., Lepper, M.R., Handelsman, M.M., Briggs, W.L., Sullivan, N., Towler, A., Bryan-Kinns, N., Healey, P.G.T., Leach, J.: Making learning fun: A taxonomy of intrinsic motivations for learning. Journal of Educational Research 98(3) (2005)Google Scholar
  39. 39.
    Miluzzo, E., Lane, N.D., Fodor, K., Peterson, R., Lu, H., Musolesi, M., Eisenman, S.B., Zheng, X., Campbell, A.T.: Sensing meets mobile social networks: the design, implementation and evaluation of the CenceMe application. In: Proceedings of the 6th ACM Conference on Embedded Network Sensor Systems, SenSys 2008, pp. 337–350. ACM, New York (2008)Google Scholar
  40. 40.
    Müller, L., Rivera-Pelayo, V., Kunzmann, C., Schmidt, A.: From Stress Awareness to Coping Strategies of Medical Staff: Supporting Reflection on Physiological Data. In: Salah, A.A., Lepri, B. (eds.) HBU 2011. LNCS, vol. 7065, pp. 94–104. Springer, Heidelberg (2011)Google Scholar
  41. 41.
    Ni, B., Wang, G., Moulin, P.: RGBD-HuDaAct: A color-depth video database for human daily activity recognition. In: Proc. IEEE Workshop on Consumer Depth Cameras for Computer Vision (2011)Google Scholar
  42. 42.
    Nijholt, A., Plass-Oude Bos, D., Reuderink, B.: Turning shortcomings into challenges: Brain-computer interfaces for games. Entertainment Computing 1(2), 85–94 (2009)CrossRefGoogle Scholar
  43. 43.
    Oliver, N.: Urban Computing and Smart Cities: Opportunities and Challenges in Modelling Large-Scale Aggregated Human Behavior. In: Salah, A.A., Lepri, B. (eds.) HBU 2011. LNCS, vol. 7065, pp. 16–17. Springer, Heidelberg (2011)Google Scholar
  44. 44.
    Orrite, C., Rodríguez, M., Montañés, M.: One-Sequence Learning of Human Actions. In: Salah, A.A., Lepri, B. (eds.) HBU 2011. LNCS, vol. 7065, pp. 40–51. Springer, Heidelberg (2011)Google Scholar
  45. 45.
    Pan, W., Aharony, N., Pentland, A.: Composite social network for predicting mobile apps installation. In: Proc. AAAI (2011)Google Scholar
  46. 46.
    Petty, R., Cacioppo, J.: The elaboration likelihood model of persuasion. Advances in Experimental Social Psychology 19(1), 123–205 (1986)CrossRefGoogle Scholar
  47. 47.
    Petty, R.E., Wegener, D.T., Fabrigar, L.R.: Attitudes and attitude change. Annual Review of Psychology 48(1), 609–647 (1997)CrossRefGoogle Scholar
  48. 48.
    Raento, M., Oulasvirta, A., Eagle, N.: Smartphones. Sociological Methods & Research 37(3), 426–454 (2009)MathSciNetCrossRefGoogle Scholar
  49. 49.
    Raghavendra, R., Del Bue, A., Cristani, M., Murino, V.: Abnormal Crowd Behavior Detection by Social Force Optimization. In: Salah, A.A., Lepri, B. (eds.) HBU 2011. LNCS, vol. 7065, pp. 137–148. Springer, Heidelberg (2011)Google Scholar
  50. 50.
    Reitberger, W., Meschtscherjakov, A., Mirlacher, T., Scherndl, T., Huber, H., Tscheligi, M.: A persuasive interactive mannequin for shop windows. In: Proceedings of the 4th International Conference on Persuasive Technology. ACM (2009)Google Scholar
  51. 51.
    Rozendaal, M., Vermeeren, A., Bekker, T., De Ridder, H.: A Research Framework for Playful Persuasion Based on Psychological Needs and Bodily Interaction. In: Salah, A.A., Lepri, B. (eds.) HBU 2011. LNCS, vol. 7065, pp. 117–126. Springer, Heidelberg (2011)Google Scholar
  52. 52.
    Salah, A.A., Gevers, T., Sebe, N., Vinciarelli, A.: Challenges of human behavior understanding. In: HBU [53], pp. 1–12 (2010)Google Scholar
  53. 53.
    Salah, A.A., Gevers, T., Sebe, N., Vinciarelli, A. (eds.): HBU 2010. LNCS, vol. 6219. Springer, Heidelberg (2010)Google Scholar
  54. 54.
    Schank, R.C., Abelson, R.P.: Scripts, plans, goals and understanding: An inquiry into human knowledge structures. Lawrence Erlbaum Associates (1977)Google Scholar
  55. 55.
    Schouten, B., Tieben, R., van de Ven, A., Schouten, D.: Human Behavior Analysis in Ambient Gaming and Playful Interaction. In: Salah, A., Gevers, T. (eds.) Computer Analysis of Human Behavior. Springer, Heidelberg (2011)Google Scholar
  56. 56.
    Schuller, B.: Voice and speech analysis in search of states and traits. In: Salah, A.A., Gevers, T. (eds.) Computer Analysis of Human Behavior, pp. 227–253. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  57. 57.
    Shapovalova, N., Fernández, C., Roca, F.X., González, J.: Semantics of human behavior in image sequences. In: Salah, A.A., Gevers, T. (eds.) Computer Analysis of Human Behavior, pp. 151–182. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  58. 58.
    van Kasteren, T., Noulas, A., Englebienne, G., Kröse, B.: Accurate activity recognition in a home setting. In: Proc. 10th Int. Conf. on Ubiquitous Computing, pp. 1–9. ACM (2008)Google Scholar
  59. 59.
    Wren, C., Ivanov, Y., Leigh, D., Westhues, J.: The MERL motion detector dataset. In: Proc. 2007 Workshop on Massive Datasets, pp. 10–14. ACM (2007)Google Scholar
  60. 60.
    Wyatt, D., Choudhury, T., Bilmes, J., Kitts, J.A.: Inferring colocation and conversation networks from privacy-sensitive audio with implications for computational social science. ACM Trans. Intell. Syst. Technol. 2, 7:1–7:41 (2011)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Albert Ali Salah
    • 1
  • Bruno Lepri
    • 2
    • 3
  • Fabio Pianesi
    • 2
  • Alex Sandy Pentland
    • 3
  1. 1.Department of Computer EngineeringBoğaziçi UniversityIstanbulTurkey
  2. 2.FBKTrentoItaly
  3. 3.MIT Media LabCambridgeUSA

Personalised recommendations