The Anticipation of Human Behavior Using "Parasitic Humanoid"

  • Hiroyuki Iizuka
  • Hideyuki Ando
  • Taro Maeda
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5612)


This paper proposes the concept of Parasitic Humanoid (PH) that can realize a wearable robot to establish intuitive interactions with wearers rather than conventional counter-intuitive ways like key-typing. It requires a different paradigm or interface technology which is called behavioral or ambient interface that can harmonize human-environment interactions to naturally lead to a more suitable state with the integration of information science and biologically inspired technology. We re-examine the use of wearable computers or devices from the viewpoint of behavioral information. Then, a possible way to realize PH is shown as integrated wearable interface devices. In order that PH establishes the harmonic interaction with wearers, a mutually anticipated interaction between a computer and human is necessary. To establish the harmonic interaction, we investigate the social interaction by experiments of human interactions where inputs and outputs of subjects are restricted in a low dimension at the behavioral level. The results of experiments are discussed with the attractor superimposition. Finally, we will discuss integrated PH system for human supports.


Ambient interface parasitic humanoid behavior-based turing test attractor superimposition 


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  1. 1.
    Ando, H., Miki, T., Inami, T., Maeda, T.: SmartFinger: Nail-mounted tactile display. In: ACM SIGGRAPH 2002 Conference Abstracts and applications, p. 78 (2002)Google Scholar
  2. 2.
    Auvray, M., Lenay, C., Stewart, J.: The attribution of intentionality in a simulated environment: the case of minimalist devices. In: Tenth Meeting of the Association for the Scientific Study of Consciousness, Oxford, UK, June 23-26 (2006)Google Scholar
  3. 3.
    Beer, R.D.: Toward the evolution of dynamical neural networks for minimally cognitive behavior. From Animals to Animats 4. In: Proceedings of the 4th International Conference on Simulation of Adaptive Behavior, pp. 421–429. MIT Press, Cambridge (1996)Google Scholar
  4. 4.
    Di Paolo, E.A.: Behavioral coordination, structural congruence and entrainment in a simulation of acoustically coupled agents. Adaptive Behavior 8, 25–46 (2000)Google Scholar
  5. 5.
    Furusawa, C., Kaneko, K.: A generic Mechanism for Adaptive Growth Rate Regulation. PLoS Computational Biology 4 (2008)Google Scholar
  6. 6.
    Hosoda, K., Mor, K., Shiroguchi, Y., Ymauchi, Y., Kashiwagi, A., Yomo, T.: Synthetic ecosystem of Escherichia coli for discovery of novel cooperative and self-adaptive algorithms. In: The 3rd International Conference on Bio-Inspired Models of Network, Information, and Computing Systems (2008)Google Scholar
  7. 7.
    Iizuka, H., Ikegami, T.: Adaptability and diversity in simulated turntaking behavior. Artificial Life 10, 361–378 (2004)CrossRefGoogle Scholar
  8. 8.
    Jacobsen, S.: Wearable Energetically Autonomous Robots, DARPA Exoskeletons for Human Performance Kick Off Meeting (2001)Google Scholar
  9. 9.
    Johansson, R.S., Westling, G.: Role of glabrous skin receptors and sensorimotor memory in automatic control of precision grip when lifting rougher or more slippery objects. Exp. Brain Res. 56, 550–564 (1984)CrossRefGoogle Scholar
  10. 10.
    Kashiwagi, A., Urabe, I., Kaneko, K., Yomo, T.: Adaptive response of a gene network to environmental changes by attractor selection. Plos One 1, e49 (2006)CrossRefGoogle Scholar
  11. 11.
    Leibnitz, K., Wakamiya, N., Murata, M.: Biologically inspired self-adaptive multi-path routing in overlay networks. Communication of the ACM 49(3), 62–67 (2006)CrossRefGoogle Scholar
  12. 12.
    Mayol, W.W., Tordoff, B., Murray, D.W.: Wearable Visual Robots. In: International Symposium on Wearable Computing (2000)Google Scholar
  13. 13.
    Murray, L., Trevarthen, C.: Emotional regulations of interactions between two-month-olds and their mothers. In: Field, T.M., Fox, N.A. (eds.) Social perception in infants, pp. 177–197. Ablex, Norwood (1985)Google Scholar
  14. 14.
    Mascaro, S., Asada, H.: Distributed Photo-Plethysmograph Fingernail Sensors: Finger Force Measurement without Haptic Obstruction. In: Proceedings of the ASME Dynamic Systems and Control Division, vol. DSC-67, pp. 73–80 (1999)Google Scholar
  15. 15.
    Nadel, J.: Imitation and imitation recognition: their social use in healthy infants and children with autism. In: The imitative mind: Development, evolution and brain bases, pp. 42–62. Cambridge University Press, Cambridge (2002)CrossRefGoogle Scholar
  16. 16.
    Nadel, J., Carchon, I., Kervella, C., Marcelli, D., Reserbat-Plantey, D.: Expectancies for Social Contingency in 2-Month-Olds. Developmental Science 2, 164–174 (1999)CrossRefGoogle Scholar
  17. 17.
    Robins, B., Dickerson, P., Dautenhahn, K.: Robots as embodied beings – Interactionally sensitive body movements in interactions among autistic children and a robot. In: Proc. IEEE Ro-man 2005, 14th IEEE International Workshop on Robot and Human Interactive Communication, pp. 54–59 (2005)Google Scholar
  18. 18.
    Scassellati, B.: Imitation and Mechanism of Joint Attention: A developmental structure for building social skills on a humanoid robot. In: Nehaniv, C.L. (ed.) CMAA 1998. LNCS (LNAI), vol. 1562, p. 176. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  19. 19.
    Tachi, S., Arai, H., Maeda, T.: Tele-Existence Simulator with Artificial Reality(1) - Design and Evaluation of a Binocular Visual Display Using Solid Models. In: IEEE International Workshop on Intelligent Robot and Systems, IROS 1998 (1988)Google Scholar
  20. 20.
    Taga, G.: A model of the neuro-musculo-skeletal system for human locomotion. Biolog. Cybern. 73, 97–111 (1995)CrossRefzbMATHGoogle Scholar
  21. 21.
    Global COE Program, Center of Excellence for Founding Ambient Information Society Infrastructure by Osaka University,

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Hiroyuki Iizuka
    • 1
  • Hideyuki Ando
    • 1
  • Taro Maeda
    • 1
  1. 1.Department of Bioinformatics Engineering, Graduate School of Information Science and TechnologyOsaka UniversityOsakaJapan

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