The European Physical Journal Special Topics

, Volume 214, Issue 1, pp 401–434 | Cite as

Socio-inspired ICT

Towards a socially grounded society-ICT symbiosis
  • A. FerschaEmail author
  • K. Farrahi
  • J. van den Hoven
  • D. Hales
  • A. Nowak
  • P. Lukowicz
  • D. Helbing
Open Access
Regular Article


Modern ICT (Information and Communication Technology) has developed a vision where the “computer” is no longer associated with the concept of a single device or a network of devices, but rather the entirety of situated services originating in a digital world, which are perceived through the physical world. It is observed that services with explicit user input and output are becoming to be replaced by a computing landscape sensing the physical world via a huge variety of sensors, and controlling it via a plethora of actuators. The nature and appearance of computing devices is changing to be hidden in the fabric of everyday life, invisibly networked, and omnipresent, with applications greatly being based on the notions of context and knowledge. Interaction with such globe spanning, modern ICT systems will presumably be more implicit, at the periphery of human attention, rather than explicit, i.e. at the focus of human attention.Socio-inspired ICT assumes that future, globe scale ICT systems should be viewed as social systems. Such a view challenges research to identify and formalize the principles of interaction and adaptation in social systems, so as to be able to ground future ICT systems on those principles. This position paper therefore is concerned with the intersection of social behaviour and modern ICT, creating or recreating social conventions and social contexts through the use of pervasive, globe-spanning, omnipresent and participative ICT.

Graphical abstract


European Physical Journal Special Topic Activity Recognition Ubiquitous Computing Pervasive Computing Social Signal 
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.


  1. 1.
  2. 2.
    Stanford encyclopedia of philosophyGoogle Scholar
  3. 3.
  4. 4.
    S. Akoush, A. Sameh, Mobile user movement prediction using bayesian learning for neural networks. In: International Wireless Communications and Mobile Computing Conference (ACM IWCMC) (Honolulu, Hawaii, USA, 2007), p. 191Google Scholar
  5. 5.
    O. Amft, H. Junker, P. Lukowicz, G. Troster, C. Schuster, Sensing muscle activities with body-worn sensors. In: Wearable and Implantable Body Sensor Networks, 2006. BSN 2006. International Workshop on (2006), p. 4, doi: 10.1109/BSN.2006.48
  6. 6.
    H.P. Bahrick, C. Shelly, J. Exper. Psychol. 56, 288 (1958)CrossRefGoogle Scholar
  7. 7.
    M. Baldauf, S. Dustdar, F. Rosenberg, Int. J. Ad Hoc Ubiquitous Comput. 2, 263 (2007) ( Scholar
  8. 8.
    B. Barber, Logic and Limits of Trust (Rutgers University Press, New Brunswick, N.J., 1983)Google Scholar
  9. 9.
    M. Barry, J. Gutknecht, I. Kulka, P. Lukowicz, T. Stricker, J. Mobile Multimedia 1, 112 (2005)Google Scholar
  10. 10.
    J.W. Bisley, M.E. Goldberg, Ann. Rev. Neurosci. 33, 1 (2010) ( Scholar
  11. 11.
    S. Bok, Lying: Moral Choice in Public and Private Life (New York: Vintage Books, 1999)Google Scholar
  12. 12.
    K.C. Bongers, A. Dijksterhuis, R. pears, J. Exper. Social Psychology 45, 468 (2009) ( Scholar
  13. 13.
  14. 14.
    D.E. Broadbent, Perception and communication, 3 edn. (Pergamon Press, Oxford, 1969)Google Scholar
  15. 15.
    K.W. Brown, R.M. Ryan, J. Personality Social Psychol. 84, 822 (2003)CrossRefGoogle Scholar
  16. 16.
    A. Bulling, D. Roggen, G. Troester, Wearable eog goggles: eye-based interaction in everyday environments. In: Proceedings of the 27th international conference extended abstracts on Human factors in computing systems, CHI ’09, (ACM, New York, NY, USA, 2009), p. 3259 (
  17. 17.
    J. Candia, M. Gonzalez, P. Wang, T. Schoenharl, G. Madey, A.L. Barabasi, J. Phys. A: Math. Theor. 41, 224 (2008)MathSciNetCrossRefGoogle Scholar
  18. 18.
    R. Carvalho, L. Buzna, W. Just, D. Helbing, D. Arrowsmith, Phys. Rev., E Stat. Nonlin. Soft Matter Phys. 85, 046 (2012)Google Scholar
  19. 19.
    B. Castellani, F. Hafferty, Sociology and complexity science: a new field of inquiry (Springer Verlag, 2009)Google Scholar
  20. 20.
    T.L. Chartrand, J.A. Bargh, J. Personality Social Psychol. 76, 893 (1999)CrossRefGoogle Scholar
  21. 21.
    T. Choudhury, A. Pentland, Sensing and modeling human networks using the socio- meter. In: IEEE International Symposium on Wearable Computers (ISWC) (Washington, USA, 2003), p. 216Google Scholar
  22. 22.
    B. Cohen, Incentives build robustness in BitTorrent (2003) (
  23. 23.
    K.J.W. Craik, British J. Psychol. General Sect. 38, 56 (1947)CrossRefGoogle Scholar
  24. 24.
    D. Crommelin, J. Frank, ERCIM News 81: Special theme, S, 28 (2010)Google Scholar
  25. 25.
    J.L. Crowley, J. Coutaz, F. Berard, F. Bérard, Comm. ACM 43, 54 (2000)CrossRefGoogle Scholar
  26. 26.
    D.L. Damos, Residual attention as a predictor of pilot performance. Human Factors 20, 435 (1978)Google Scholar
  27. 27.
    R.K. Dash, N.R. Jennings, D.C. Parkes, IEEE Intell. Syst. 18, 40 (2003) ( Scholar
  28. 28.
    F. De Brigard, J. Prinz, Wiley Interdisciplinary Rev. Cognitive Sci. 1, 51 (2010) ( Scholar
  29. 29.
  30. 30.
    M. Deutsch, Nebraska Symposium on Motivation., chap. Cooperation and Trust: Some Theoretical Notes (Nebraska University Press, 1962)Google Scholar
  31. 31.
    A.K. Dey, Personal Ubiquitous Comput. 5, 4 (2001) ( Scholar
  32. 32.
    A. Dijksterhuis, H. Aarts, Ann. Rev. Psychol. 61 (2010) (
  33. 33.
    T. Do, D. Gatica-Perez, By their apps you shall understand them: mining large-scale patterns of mobile phone usage. In: 9th International Conference on Mobile and Ubiquitous Multimedia (MUM) (Limassol, Cyprus, 2010)Google Scholar
  34. 34.
    R.J. Dolan, Science 298, 1191 (2002)ADSCrossRefGoogle Scholar
  35. 35.
    A. Dufresne, F. Courtemanche, S. Prom Tep, S. Senecal, Physiological measures, eye tracking and task analysis to track user reactions in user generated content. In: 7th Internationcal Conference on Methods and Techniques in Behavioral Research (Measuring Behavior, 2010), p. 218Google Scholar
  36. 36.
    N. Eagle, Machine perception and learning of complex social systems. Ph.D. thesis, Massachusetts Institute of Technology, 2005Google Scholar
  37. 37.
    N. Eagle, M. Macy, R. Claxton, Science 328, 1029 (2010)MathSciNetADSzbMATHCrossRefGoogle Scholar
  38. 38.
  39. 39.
    N. Eagle, A. Pentland, Behavioral Ecol. Sociobiol. 63, 1057 (2009)CrossRefGoogle Scholar
  40. 40.
    N. Eagle, A. Pentland, D. Lazer, Proc. National Acad. Sci. (PNAS) 106, 274 (2009)CrossRefGoogle Scholar
  41. 41.
    D. Erdogmus, A. Adami, M. Pavel, T. Lan, S. Mathan, S. Whitlow, M. Dorneich, Cognitive state estimation based on eeg for augmented cognition. In: Neural Engineering, 2005. Conference Proceedings. 2nd International IEEE EMBS Conference on (2005), p. 566 (
  42. 42.
    J. Exeler, J. Mueller, M. Buzeck, A. Krueger, Reflectivesigns: Digital signs that adapt to audience attention. In: Pervasive 2009, International Conference on Pervasive Computing (Pervasive-09), Nara, Japan, vol. 5538/2009 (Springer Berlin/Heidelberg, 2009), p. 17 (
  43. 43.
    K. Farrahi, R. Emonet, A. Ferscha, Socio-technical network analysis from wearable interactions. In: Wearable Computers (ISWC), 2012 16th International Symposium on (IEEE, 2012), p. 9Google Scholar
  44. 44.
    K. Farrahi, D. Gatica-Perez, IEEE J. Selected Topics Signal Proc. (J-STSP) 4, 746 (2010)ADSCrossRefGoogle Scholar
  45. 45.
    K. Farrahi, D. Gatica-Perez, ACM Transactions on Intelligent Systems and Technology, Special Issue on Intelligent Systems for Activity Recognition 2, 3:1 (2011)Google Scholar
  46. 46.
    D. Fernandez-Duque, G. Grossi, I.M. Thornton, H.J. Neville, J. Cognitive Neuroscience 15, 491 (2003) ( Scholar
  47. 47.
    A. Ferscha, From individual to collective attention–models and dynamicsGoogle Scholar
  48. 48.
    A. Ferscha, 20 years past weiser: What’s next? Pervasive Computing, IEEE 11, 52 (2012)Google Scholar
  49. 49.
    A. Ferscha, N. Davies, A. Schmidt, N. Streitz, Procedia Computer Science 7, 88 (2011)CrossRefGoogle Scholar
  50. 50.
    A. Ferscha, S. Vogl, Pervasive web access via public communication walls. In: Proceedings of the 1st International Conference on Pervasive Computing (Pervasive 2002), vol. 2414 (Springer LNCS, Zurich, Switzerland, 2002), p. 84 (
  51. 51.
    A. Ferscha, K. Zia, Lifebelt: Silent directional guidance for crowd evacuation. In: Wearable Computers, 2009. ISWC’09. International Symposium on (IEEE, 2009), p. 19Google Scholar
  52. 52.
    A. Ferscha, K. Zia, Pervasive Computing, IEEE 9, 33 (2010)CrossRefGoogle Scholar
  53. 53.
    A. Ferscha, K. Zia, A. Riener, A. Sharpanskykh, Procedia Comp. Sci. 7, 235 (2011)CrossRefGoogle Scholar
  54. 54.
    D. Gambetta, Trust: Making and Breaking Cooperative Relations, chap. Can We Trust Trust? (Department of Sociology, University of Oxford, 2000), p. 213Google Scholar
  55. 55.
    J.J. Gibson, The Senses Considered as Perceptual Systems (Houghton Mifflin Company 1966) (
  56. 56.
    G.N. Gilbert, Agent-based models. Sage Publications, Inc. (2008)Google Scholar
  57. 57.
    R. Golembiewski, M. McConkie, Theories of group process., chap. The centrality of interpersonal trust in group process (New York: Wiley, 1988)Google Scholar
  58. 58.
    M.C. Gonzalez, C.A. Hidalgo, A.L. Barabasi, Nature 453, 779 (2008)ADSCrossRefGoogle Scholar
  59. 59.
    D. Good, Trust: Making and Breaking Cooperative Relations., chap. Individuals, Interpersonal Relations, and Trust. (Department of Sociology, University of Oxford, 2000), p. 31 (
  60. 60.
    R.L. Gregory, The intelligent eye (1970)Google Scholar
  61. 61.
    G. Groh, E. Lehmann, T. Wang, S. Huber, F. Hammerl, Applications for social situation models. In: Proceeding of the IADIS International Conference on Wireless Applications and Computing (2010)Google Scholar
  62. 62.
    M. Haslgruebler, C. Holzmann, DarSens: A Framework for Distributed Activity Recognition from Body-Worn Sensors. In: Proceedings of the Fifth International Conference on Body Area Networks (BodyNets’10) (Corfu Island, Greece, 2010)Google Scholar
  63. 63.
    S. Helal, W. Mann, H. El-Zabadani, J. King, Y. Kaddoura, E. Jansen, Computer 38, 50 (2005)CrossRefGoogle Scholar
  64. 64.
    D. Helbing, Social Self-Organization: Agent-Based Simulations and Experiments to Study Emergent Social Behavior, Understanding Complex Systems (Springer 2012)Google Scholar
  65. 65.
    J. Hightower, S. Consolvo, A. Lamarca, I. Smith, J. Hughes, Learning and recognizing the places we go. In: Ubiquitous Computing (UbiComp), Tokyo, Japan (2005), p. 159Google Scholar
  66. 66.
    G. Hoelzl, M. Kurz, A. Ferscha, Goal oriented opportunistic recognition of high-level composed activities using dynamically configured hidden markov models. Procedia Computer Science 10, 308 (2012)Google Scholar
  67. 67.
    G. Hoelzl, M. Kurz, P. Halbmayer, J. Erhart, M. Matscheko, A. Ferscha, S. Eisl, J. Kaltenleithner, Locomotion@ location: When the rubber hits the road. The 9th International Conference on Autonomic Computing (ICAC2012), San Jose, California, USA (2012)Google Scholar
  68. 68.
    W. James, The Principles of Psychology, vol. 1 (Dover Publications, 1950) (
  69. 69.
  70. 70.
    D. Kahneman, Attention and effort (Prentice-Hall, Englewood Cliffs, N.J., 1973)Google Scholar
  71. 71.
    D. Kahneman, Amer. Econom. Rev. 93, 1449 (2003)CrossRefGoogle Scholar
  72. 72.
    J.O. Kephart, D.M. Chess, The vision of autonomic computing. Computer 36, 41 (2003)Google Scholar
  73. 73.
    A. Kesting, M. Treiber, D. Helbing, IEEE Trans. Intell. Transportation Syst. 11, 172 (2010)CrossRefGoogle Scholar
  74. 74.
    A. Kesting, M. Treiber, M. Schonhof, D. Helbing, Transportation Research Part C: Emerging Technologies 16, 668 (2008) ( Scholar
  75. 75.
    K.N. Kirschner, A. Arnold, A. Maass, ERCIM News 81: Special theme, S, 22 (2010)Google Scholar
  76. 76.
    N. Kiukkonen, J. Blom, O. Dousse, D. Gatica-Perez, J. Laurila, Towards rich mobile phone datasets: Lausanne data collection campaign. In: Proceedings ACM International Conference on Pervasive Services (ICPS), (Berlin, Germany, 2010)Google Scholar
  77. 77.
    C. Koch, N. Tsuchiya, Trends Cognitive Sci. 16, 103 (2012)CrossRefGoogle Scholar
  78. 78.
    O. Krejcar, Using of ubiquitous computing principles to develop a mobile user adaptive system framework. In: Roedunet International Conference (RoEduNet), 2010 9th (2010), p. 352Google Scholar
  79. 79.
    J. Krumm, E. Horvitz, Predestination: Inferring destinations from partial trajectories. In: Ubiquitous Computing (UbiComp), (California, USA, 2006)Google Scholar
  80. 80.
    M. Kumar, S. Das, Pervasive computing: Enabling technologies and challenges, edited by A. Zomaya, Handbook of Nature-Inspired and Innovative Computing (Springer, US, 2006), p. 613Google Scholar
  81. 81.
    K. Kunze, G. Bahle, P. Lukowicz, K. Partridge, Can magnetic field sensors replace gyroscopes in wearable sensing applications? In: Wearable Computers (ISWC), 2010 International Symposium on (2010), p. 1Google Scholar
  82. 82.
    M. Kurz, G. Hölzl, A. Ferscha, Dynamic adaptation of opportunistic sensor configurations for continuous and accurate activity recognition. In: ADAPTIVE 2012, The Fourth International Conference on Adaptive and Self-Adaptive Systems and Applications (2012), p. 13Google Scholar
  83. 83.
    M. Kurz, G. Hölzl, A. Ferscha, A. Calatroni, D. Roggen, G. Tröster, Real-time transfer and evaluation of activity recognition capabilities in an opportunistic system. In: ADAPTIVE 2011, The Third International Conference on Adaptive and Self-Adaptive Systems and Applications (2011), p. 73Google Scholar
  84. 84.
    M. Kurz, G. Hölzl, A. Ferscha, A. Calatroni, D. Roggen, G. Tröster, H. Sagha, R. Chavarriaga, J. Millán, D. Bannach, et al., International Journal of Sensors, Wireless Communications and Control, Special Issue on Autonomic and Opportunistic Communications 1 (2011)Google Scholar
  85. 85.
    S. Laemmer, D. Helbing, Self-control of traffic lights and vehicle flows in urban road networks. 0802.0403 (2008) (
  86. 86.
    V. Lamme, Neural Networks 17, 861 (2004)zbMATHCrossRefGoogle Scholar
  87. 87.
    N. Lavie, A. Hirst, J.W.d. Fockert, E. Viding, J. Exper. Psychol. General 133, 339 (2004)CrossRefGoogle Scholar
  88. 88.
    D. Lazer, A. Pentland, L. Adamic, S. Aral, A. Barabasi, D. Brewer, N. Christakis, N. Contractor, J. Fowler, M. Gutmann, T. Jebara, G. King, M. Macy, D. Roy, M. Van Alstyne, Science 323, 721 (2009)CrossRefGoogle Scholar
  89. 89.
    J. Letchner, D. Fox, A. LaMarca, Large-scale localization from wireless signal strength. In: National Conference on Artificial Intelligence (AAAI) (Pittsburgh, Pennsylvania, USA, 2005), p. 15Google Scholar
  90. 90.
    L. Liao, D. Fox, H. Kautz, Location-based activity recognition. In: Advances in Neural Information Processing Systems (NIPS) (Vancouver, Canada, 2006), p. 787Google Scholar
  91. 91.
    M. Loecher, T. Jebara, CitySense: Multiscale space time clustering of gps points and trajectories. In: Proceedings of the Joint Statistical Meeting (2009)Google Scholar
  92. 92.
    H. Lu, W. Pan, N.D. Lane, T. Choudhury, A.T. Campbell, Soundsense: scalable sound sensing for people-centric applications on mobile phones. In: Proc. of the 7th International Conference on Mobile Systems, Applications, and Services (MobiSys) (New York, NY, USA, 2009), p. 165Google Scholar
  93. 93.
    N. Luhmann., Trust: Making and Breaking Cooperative Relations., chap. Familiarity, Confidence, Trust: Problems and Alternatives (Department of Sociology, University of Oxford, 2000)Google Scholar
  94. 94.
    N. Luhmann, G. Poggi, Trust and power (John Wiley and Sons, 1979)Google Scholar
  95. 95.
    P. Lukowicz, O. Amft, D. Roggen, Cheng, J. Comp. 43, 92 (2010)Google Scholar
  96. 96.
    P. Lukowicz, S. Pentland, A. Ferscha, Pervasive Computing, IEEE 11, 32 (2012)CrossRefGoogle Scholar
  97. 97.
    J. MacKillop, E.J. Anderson, J. Psychopathol. Behav. Assess. 29, 289 (2007)CrossRefGoogle Scholar
  98. 98.
    A. Madan, M. Cebrian, S. Moturu, K. Farrahi, A. Pentland, Sensing the “health state” of our society. IEEE Pervasive Computing, Speciall Issue on Large-Scale Opportunistic Sensing (2011)Google Scholar
  99. 99.
    A. Madan, K. Farrahi, D. Gatica-Perez, A. Pentland, Pervasive sensing to model political opinions in face-to-face networks. In: Pervasive (San Francisco, USA, 2011)Google Scholar
  100. 100.
    A. Madan, A. Pentland, Vibefones: Socially aware mobile phones. In: IEEE International Symposium on Wearable Computers (ISWC), (Montreux, Switzerland 2006)Google Scholar
  101. 101.
    A. Malatras, B. Hirsbrunner, A Context-Aware Framework to Enable Adaptation in Pervasive Computing Environments. International Conference on Network-Based Information Systems (NBiS 2009) 0, 182 (2009) ( Scholar
  102. 102.
    Maner JonC.N.B.R.F.S.M. DeWall, J. Personality Social Psychol. 92, 42 (2007)CrossRefGoogle Scholar
  103. 103.
    A. Markarian, J. Favela, M. Tentori, L. Castro, Seamless interaction among heterogeneous devices in support for co-located collaboration, edited by Y. Dimitriadis, I. Zigurs, E. Gomez-Sanchez, Groupware: Design, Implementation, and Use, Lecture Notes in Computer Science, vol. 4154 (Springer Berlin, Heidelberg, 2006), p. 389Google Scholar
  104. 104.
    R. Matthews, N. McDonald, P. Hervieux, P. Turner, M. Steindorf, A wearable physiological sensor suite for unobtrusive monitoring of physiological and cognitive state. In: Engineering in Medicine and Biology Society, 2007. EMBS 2007. 29th Annual International Conference of the IEEE (IEEE, 2007), p. 5276 (
  105. 105.
    P.A. McCormick, J. Exper. Psychol. Human Perception Perf. 23, 168 (1997) ( Scholar
  106. 106.
    D. McIlwraith, J. Pansiot, G.Z. Yang, Wearable and ambient sensor fusion for the characterisation of human motion. In: Intelligent Robots and Systems (IROS), 2010 IEEE/RSJ International Conference on (2010), p. 5505Google Scholar
  107. 107.
    S.A. McLeod, Visual perception ( (2007)
  108. 108.
    E. Miluzzo, N.D. Lane, S.B. Eisenman, A.T. Campbell, Cenceme: injecting sensing presence into social networking applications. In: Proceedings of the 2nd European conference on Smart sensing and context, EuroSSC’07 (Springer-Verlag, Berlin, Heidelberg, 2007), p. 1Google Scholar
  109. 109.
    MIT Technology Review: 10 emerging technologies 2008 ( (2008)
  110. 110.
    R. Montoliu, D. Gatica-Perez, Discovering human places of interest from multimodal mobile phone data. In: 9th International Conference on Mobile and Ubiquitous Multimedia (MUM), (Limassol, Cyprus, 2010)Google Scholar
  111. 111.
    H. Nakashima, H. Aghajan, J.C. Augusto (eds.), Handbook of Ambient Intelligence and Smart Environments (Springer, 2010) ISBN: 978-0-387-93807-3Google Scholar
  112. 112.
    A. Ohman, A. Flykt, Esteves, F General 130, 466 (2001)Google Scholar
  113. 113.
    J. Okuda, S.J. Gilbert, P.W. Burgess, C.D. Frith, J.S. Simons, Neuropsychologia 49, 2258 (2011)CrossRefGoogle Scholar
  114. 114.
    E. Ostrom, Governing the commons-The evolution of institutions for collective actions (Political economy of institutions and decisions, 1990)Google Scholar
  115. 115.
    V. Otsason, A. Varshavsky, A. Lamarca, E. de Lara, Accurate gsm indoor localization. In: Ubiquitous Computing (UbiComp) (Springer Berlin/Heidelberg, 2005), p. 141Google Scholar
  116. 116.
    C.H. Park, K.B. Sim, F. Harashima, Human adaptive system model development merged with context and emotion information. In: Industrial Electronics Society, 2004. IECON 2004. 30th Annual Conference of IEEE, vol. 1 (2004), p. 633Google Scholar
  117. 117.
    D. Patterson, L. Liao, D. Fox, H. Kautz, Inferring high-level behavior from low-level sensors. In: Ubiquitous Computing (UbiComp) (Seattle, USA, 2003), p. 73Google Scholar
  118. 118.
    A.S. Pentland, Socially aware computation and communication. Computer 38, 33 (2005) ( Scholar
  119. 119.
    E.A. Phelps, S. Ling, M. Carrasco, Psychol. Sci. 17, 292 (2006) 10.1111/j.1467-9280.2006.01701.xCrossRefGoogle Scholar
  120. 120.
    M. Philipose, K. Fishkin, M. Perkowitz, D. Patterson, D. Fox, H. Kautz, D. Hahnel, Pervasive Computing, IEEE 3, 50 (2004)CrossRefGoogle Scholar
  121. 121.
    S. Phithakkitnukoon, T.W. Leong, Z. Smoreda, P. Olivier, PLoS ONE 7, e45,745 (2012)Google Scholar
  122. 122.
    R.W. Picard, Affective computing (MIT Press, Cambridge, MA, USA, 1997)Google Scholar
  123. 123.
    I. Poggi, F. D’Errico, Cognitive modelling of human social signals. In: Proceedings of the 2nd international workshop on Social signal processing, SSPW ’10 (2010), p. 21Google Scholar
  124. 124.
    M.I. Posner, Quarterly J. Exper. Psychol. 32, 3 (1980)CrossRefGoogle Scholar
  125. 125.
    S.A.F. Pour, User identification roadmap towards 2020. Master thesis no. muc-2008-01, Department of Interaction and System Design, School of Engineering (Blekinge Institute of Technology, Sweden, 2008)Google Scholar
  126. 126.
    R. Rahman, T. Vinkó, D. Hales, J. Pouwelse, H. Sips, Commun. Rev. 41, 182 (2011) ( Scholar
  127. 127.
    S. Reddy, J. Burke, D. Estrin, M. Hansen, M. Strivastava, Using mobile phones to determine transportation mode. In: IEEE International Symposium on Wearable Computers (ISWC), (Pittsburgh, Pennsylvania, USA, 2008)Google Scholar
  128. 128.
    A. Riener, Continuous Authentication based on Biometrics: Data, Models, and Metrics, chap. Sitting Postures & Electrocardiograms: A Method for Continuous and Unobtrusive Driver Authentication, IGI Global (2011), p. 30, ISBN: n.a., (in press)Google Scholar
  129. 129.
    A. Riener, M. Aly, A. Ferscha, Heart on the road: HRV analysis for monitoring a driver’s affective state. In: 1st International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI 2009) ACM Digital Library, University of Duisburg-Essen, Essen, Germany (2009), p. 8, ISBN: 978-1-60558-571Google Scholar
  130. 130.
    A. Riener, A. Ferscha, Supporting Implicit Human-to-Vehicle Interaction: Driver Identification from Sitting Postures. In: The First Annual International Symposium on Vehicular Computing Systems (ISVCS 2008), July 22–24, 2008, Trinity College Dublin, Ireland ACM Digital Library (2008), p. 10, ISBN: 978-963-9799-27-1Google Scholar
  131. 131.
    H. Rimminen, J. Lindstroem, M. Linnavuo, R. Sepponen, Trans. Info. Tech. Biomed. 14, 1475 (2010) ( Scholar
  132. 132.
    D. Roggen, A. Calatroni, K. Förster, G. Tröster, P. Lukowicz, D. Bannach, A. Ferscha, M. Kurz, G. Hölzl, H. Sagha, et al., Procedia Computer Science 7, 173 (2011)CrossRefGoogle Scholar
  133. 133.
    M. Sarter, W.J. Gehring, R. Kozak, Brain Res. Rev. 51, 145 (2006)CrossRefGoogle Scholar
  134. 134.
    B. Schilit, N. Adams, R. Want, Context-aware computing applications. In: Proceedings of the 1994 First Workshop on Mobile Computing Systems and Applications, WMCSA ’94, IEEE Computer Society, Washington, DC, USA (1994), p. 85 (
  135. 135.
    W. Schneider, R.M. Shiffrin, Psychological Rev. 84, 1 (1977)CrossRefGoogle Scholar
  136. 136.
    D. Schuster, A. Rosi, M. Mamei, T. Springer, M. Endler, F. Zambonelli, Pervasive social context - taxonomy and survey. ACM Transactions on Intelligent Systems and Technology (TIST) (2012)Google Scholar
  137. 137.
    H.A. Simon, Quart. J. Econom 69, 99 (1955)CrossRefGoogle Scholar
  138. 138.
    T. Sohn, A. Varshavsky, A. Lamarca, M. Chen, T. Choudhury, I. Smith, S. Consolvo, J. Hightower, W. Griswold, E. de Lara, Mobility detection using everyday gsm traces. In: Ubiquitous Computing (UbiComp) (California, USA, 2006), p. 212Google Scholar
  139. 139.
    A. Soro, G. Paddeu, M. Lobina, Multitouch sensing for collaborative interactive walls, edited by P. Forbrig, F. Paterno, A. Pejtersen, Human-Computer Interaction Symposium, IFIP International Federation for Information Processing, vol. 272 (Springer, Boston, 2008), p. 207Google Scholar
  140. 140.
    J. Taylor, Progr. Neurobiol. 71, 305 (2003)CrossRefGoogle Scholar
  141. 141.
    H. Tirri, Pervasive Technology that Changed the World (2010), Keynote talk at the 8th International Conference On Pervasive ComputingGoogle Scholar
  142. 142.
    A.M. Treisman, G. Gelade, Cognitive Psychol. 12, 97 (1980)CrossRefGoogle Scholar
  143. 143.
    J. Treur, On human aspects in ambient intelligence, edited by M. Muehlhaeuser, A. Ferscha, E. Aitenbichler, Constructing Ambient Intelligence, Communications in Computer and Information Science, vol. 11 (Springer, Berlin, Heidelberg, 2008), p. 262Google Scholar
  144. 144.
    M. Turk, Comm. ACM 43, 33 (2000)CrossRefGoogle Scholar
  145. 145.
    J. Twenge, K. Catanese, R. Baumeister, J. Personality Social Psychol. 83, 606 (2002)CrossRefGoogle Scholar
  146. 146.
    N.T. Van Dam, M. Earleywine, A. Borders, Personality and Individual Differences 49, 805 (2010) ( Scholar
  147. 147.
    H. Verkasalo, C. Lopez-Nicolas, F. Molina-Castillo, H. Bouwman, Analysis of users and non-users of smartphone applications. Telematics and Informatics 27, 242 (2010)Google Scholar
  148. 148.
    A. Vinciarelli, M. Pantic, H. Bourlard, Image Vision Comput. 27, 1743 (2009)CrossRefGoogle Scholar
  149. 149.
    A. Vinciarelli, M. Pantic, D. Heylen, C. Pelachaud, I. Poggi, F. D’Errico, Bridging the gap between social animal and unsocial machine: A survey of social signal processing. IEEE Transactions on Affective Computing (2012)Google Scholar
  150. 150.
    P. Wang, M.C. Gonzalez, C.A. Hidalgo, A.L. Barabasi, Science 324, 1071 (2009)ADSCrossRefGoogle Scholar
  151. 151.
    M. Weiser, Comput. Commun. Rev. 3, 3 (1999) ( Scholar
  152. 152.
    A. Wesolowski, N. Eagle, Parameterizing the dynamics of slums. In: AAAI Spring Symposium on Artificial Intelligence for Development (AI-D) (2010)Google Scholar
  153. 153.
    C.D. Wickens, J.S. McCarley, Applied attention theory (CRC Press, Boca Raton, 2008) (
  154. 154.
    M. Wirz, D. Roggen, G. Troester, Decentralized detection of group formations from wearable acceleration sensors. In: Proceedings of the 2009 IEEE International Conference on Social Computing (IEEE Press, 2009)Google Scholar
  155. 155.
    M. Wirz, D. Roggen, G. Troester, A Methodology towards the Detection of Collective Behavior Patterns by Means of Body-Worn Sensors. In: UbiLarge workshop at the 8th International Conference on Pervasive Computing (2010), p. 4Google Scholar
  156. 156.
    M. Wirz, D. Roggen, G. Troester, A wearable, ambient sound-based approach for infrastructureless fuzzy proximity estimation. In: Proceedings of the 14th IEEE International Symposium on Wearable Computers (ISWC 2010), IEEE Computer Society (2010)Google Scholar
  157. 157.
    M. Woollacott, A. Shumway-Cook, Gait & Posture 16, 1 (2002)CrossRefGoogle Scholar
  158. 158.
    J. Yiend, Cognition & Emotion 24, 3 (2010)CrossRefGoogle Scholar
  159. 159.
    G. Yogev-Seligmann, J.M. Hausdorff, N. Giladi, Movement Disorders 23, 329 (2008) ( Scholar
  160. 160.
    J. Zhou, J. Sun, K. Athukorala, D. Wijekoon, Pervasive social computing: Augmenting five facets of human intelligence. In: Ubiquitous Intelligence Computing and 7th International Conference on Autonomic Trusted Computing (UIC/ATC), 2010 7th International Conference on (2010), p. 1 10.1109/UIC-ATC.2010.35Google Scholar

Copyright information

© The Author(s) 2012

Authors and Affiliations

  • A. Ferscha
    • 1
    Email author
  • K. Farrahi
    • 1
  • J. van den Hoven
    • 2
  • D. Hales
    • 3
  • A. Nowak
    • 4
  • P. Lukowicz
    • 5
  • D. Helbing
    • 6
  1. 1.Inst. f. Pervasive ComputingUniversity of Linz (JKU)LinzAustria
  2. 2.Philosophy SectionDelft University of TechnologyDelftThe Netherlands
  3. 3.The Open UniversityLondonUK
  4. 4.Department of PsychologyUniversity of WarsawWarsawPoland
  5. 5.DFKIKaiserslauternGermany
  6. 6.ETH ZürichZürichSwitzerland

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