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Assisted Man-Machine Interaction

  • Karl-Friedrich Kraiss
Part of the Signals and Communication Technology book series (SCT)

Keywords

Manual Control Differential Global Position System Differential Global Position System Menu Item Plan Recognition 
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.

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References

  1. 1.
    Akyol, S., Libuda, L., and Kraiss, K.-F. Multimodale Benutzung adaptiver Kfz-Bordsysteme. In Jürgensohn, T. and Timpe, K.-P., editors, Kraftfahrzeugführung, pages 137–154. Springer, 2001.Google Scholar
  2. 2.
    Albrecht, D. W., Zukerman, I., and Nicholson, A. Bayesian Models for Keyhole Plan Recognition in an Adventure Game. User Modeling and User-Adapted Interaction, 8(1–2):5–47, 1998.CrossRefGoogle Scholar
  3. 3.
    Arnott, A. L. and Javed, M. Y. Probabilistic Character Disambiguation for Reduced Keyboards Using Small Text Samples. Augmentative and Alternative Communication, 8(3):215–223, 1992.CrossRefGoogle Scholar
  4. 4.
    Azuma, R. T. A Survey of Augmented Reality. Presence: Teleoperators and Virtual Environments, 6(4):355–385, 1997.Google Scholar
  5. 5.
    Belz, S. M. A Simulator-Based Investigation of Visual, Auditory, and Mixed-Modality Display of Vehicle Dynamic State Information to Commercial Motor Vehicle Operators. Master’s thesis, Virginia Polytechnic Institute, Blacksburg, Virginia, USA, 1997.Google Scholar
  6. 6.
    Bien, Z. Z. and Stefanov, D. Advances in Rehabilitation Robotics. Springer, 2004.Google Scholar
  7. 7.
    Boff, K. R., Kaufmann, L., and Thomas, J. P. Handbook of Perception and Human Performance. Wiley, 1986.Google Scholar
  8. 8.
    Breazeal, C. Recognition of Affective Communicative Intent in Robot-Directed Speech. Autonomous Robots, 12(1):83–104, 2002.zbMATHCrossRefGoogle Scholar
  9. 9.
    Canberry, S. Techniques for Plan Recognition. User Modeling and User-Adapted Interaction, 11(1–2):31–48, 2001.CrossRefGoogle Scholar
  10. 10.
    Charniak, E. and Goldmann, R. B. A Bayesian Model of Plan Recognition. Artificial Intelligence, 64(1):53–79, 1992.CrossRefGoogle Scholar
  11. 11.
    Dickmanns, E. D. The Development of Machine Vision for Road Vehicles in the last Decade. In Proceedings of the IEEE Intelligent Vehicle Symposium, volume 1, pages 268–281. Versailles, June 17–21 2002.Google Scholar
  12. 12.
    Friedrich, W. ARVIKA Augmented Reality für Entwicklung, Produktion und Service. Publicis Corporate Publishing, Erlangen, 2004.Google Scholar
  13. 13.
    Garrel, U., Otto, H.-J., and Onken, R. Adaptive Modeling of the Skill-and Rule-Based Driver Behavior. In The Driver in the 21st Century, VDI-Berichte 1613, pages 239–261. Berlin, 3.–4. Mai 2001.Google Scholar
  14. 14.
    Haykin, S. Adaptive Filter Theory. Prentice-Hall, Englewood Cliffs, NJ, 2nd edition, 1991.zbMATHGoogle Scholar
  15. 15.
    Haykin, S. Neural Networks: A Comprehensive Foundation. Prentice Hall, Upper Saddle River, NJ, 2nd edition, 1999.zbMATHGoogle Scholar
  16. 16.
    Hirzinger, G., Brunner, B., Dietrich, J., and Heindl, J. Sensor-Based Space Robotics-ROTEX and its Telerobotic Features. IEEE Transactions on Robotics and Automation (Special Issue on Space Robotics), 9(5):649–663, October 1993.CrossRefGoogle Scholar
  17. 17.
    Hofmann, M. Intentionsbasierte maschinelle Interpretation von Benutzeraktionen. Dissertation, Technische Universität München., 2003.Google Scholar
  18. 18.
    Hofmann, M. and Lang, M. User Appropriate Plan Recognition for Adaptive Interfaces. In Smith, M. J., editor, Usability Evaluation and Interface Design: Cognitive Engineering, Intelligent Agents and Virtual Reality. Proceedings of the 9th International Conferenceon Human-Computer Interaction, volume 1, pages 1130–1134. New Orleans, August 5–10 2001.Google Scholar
  19. 19.
    Jackson, C. A Method for the Direct Measurement of Crossover Model Parameters. IEEE MMS, 10(1):27–33, March 1969.Google Scholar
  20. 20.
    Jameson, A. Numerical Uncertainty Management in User and Student Modeling: An Overview of Systems and Issues. User Modeling and User-Adapted Interaction. The Journal of Personalization Research, 5(4):193–251, 1995.MathSciNetCrossRefGoogle Scholar
  21. 21.
    Ji, Q. and Yang, X. Real-Time Eye, Gaze, and Face Pose Tracking for Monitoring Driver Vigilance. Real-Time Imaging, 8(5):357–377, 2002.MathSciNetCrossRefzbMATHGoogle Scholar
  22. 22.
    Knoll, P. The Night Sensitive Vehicle. In VDI-Report 1768, pages 247–256. VDI, 2003.Google Scholar
  23. 23.
    Kragic, D. and Christensen, H. F. Robust Visual Servoing. The International Journal of Robotics Research, 22(10–11):923 ff, 2003.Google Scholar
  24. 24.
    Kraiss, K.-F. Ergonomie, chapter Mensch-Maschine Dialog, pages 446–458. Carl Hanser, München, 3rd edition, 1985.Google Scholar
  25. 25.
    Kraiss, K.-F. Fahrzeug-und Prozessführung: Kognitives Verhalten des Menschen und Entscheidungshilfen. Springer Verlag, Berlin, 1985.Google Scholar
  26. 26.
    Kraiss, K.-F. Implementation of User-Adaptive Assistants with Neural Operator Models. Control Engineering Practice, 3(2):249–256, 1995.CrossRefGoogle Scholar
  27. 27.
    Kraiss, K.-F. and Hamacher, N. Concepts of User Centered Automation. Aerospace Science Technology, 5(8):505–510, 2001.CrossRefzbMATHGoogle Scholar
  28. 28.
    Kushler, C. AAC Using a Reduced Keyboard. In Proceedings of the CSUN California State University Conference, Technology and Persons with Disabilities Conference. Los Angeles, March 1998.Google Scholar
  29. 29.
    Larimore, M. G., Johnson, C. R., and Treichler, J. R. Theory and Design of Adaptive Filters. Prentice Hall, 1st edition, 2001.Google Scholar
  30. 30.
    Mann, M. and Popken, M. Auslegung einer fahreroptimierten Mensch-Maschine-Schnittstelle am Beispiel eines Querführungsassistenten. In GZVB, editor, 5. Braunschweiger Symposium “Automatisierungs-und Assistenzsystemefuer Transportmittel”, pages 182–108. 17.–18. Februar 2004.Google Scholar
  31. 31.
    Matsikis, A., Zoumpoulidis, T., Broicher, F., and Kraiss, K.-F. Learning Object-Specific Vision-Based Manipulation in Virtual Environments. In IEEE Proceedings of the 11th International Workshop on Robot and Human Interactive Communication ROMAN 2002, pages 204–210. Berlin, September 25–27 2002.Google Scholar
  32. 32.
    Neumerkel, D., Rammelt, P., Reichardt, D., Stolzmann, W., and Vogler, A. Fahrermodelle-Ein Schlüssel für unfallfreies Fahren? Künstliche Intelligenz, 3:34–36, 2002.Google Scholar
  33. 33.
    Nickerson, R. S. On Conversational Interaction with Computers. In Treu, S., editor, Proceedings of the ACM/SIGGRAPH Workshop on User-Oriented Design of Interactive Graphics Systems, pages 101–113. Pittsburgh, PA, October 14–15 1976.Google Scholar
  34. 34.
    Pao, Y. Adaptive Pattern Recognition and Neural Networks. Addison-Wesley, 1989.Google Scholar
  35. 35.
    Reintsema, D., Preusche, C., Ortmaier, T., and Hirzinger, G. Towards High Fidelity Telepresence in Space and Surgery Robotics. Presence-Teleoperators and Virtual Environments, 13(1):77–98, 2004.CrossRefGoogle Scholar
  36. 36.
    Schmidt, A. and Gellersen, H.W. Nutzung von Kontext in ubiquitären Informationssystemen. it+ti-Informationstechnik und technische Informatik, Sonderheft: UbiquitousComputing-der allgegenwärtige Computer, 43(2):83–90, 2001.Google Scholar
  37. 37.
    Schraut, M. Umgebungserfassung auf Basis lernender digitaler Karten zur vorausschauenden Konditionierung von Fahrerassistanzsystemen. Ph.D. thesis, Technische Universität München, 2000.Google Scholar
  38. 38.
    Shneiderman, B. and Plaisant, C. Designing the User Interface. Strategies for Effective Human-Computer Interaction. Addison Wesley, 4th edition, 2004.Google Scholar
  39. 39.
    Specht, D. F. A General Regression Neural Network. IEEE Transactions on Neural Networks, 2(6):568–576, 1991.CrossRefGoogle Scholar
  40. 40.
    Velger, M., Grunwald, A., and Merhav, S. J. Adaptive Filtering of Biodynamic Stick Feedthrough in ManipulationTaskson Board Moving Platforms. AIAA Journal of Guidance, Control, and Dynamics, 11(2):153–158, 1988.CrossRefzbMATHGoogle Scholar
  41. 41.
    Wahlster, W., Reitiger, N., and Blocher, A. SMARTKOM: Multimodal Communication with a Life-Like Character. In Proceedings of the 7th European Conference on Speech CommunicationandTechnology, volume 3, pages 1547–1550. Aalborg, Denmark, September 3–7 2001.Google Scholar
  42. 42.
    Wickens, C. D. Engineering Psychology and Human Performance. Columbus. Merrill, 1984.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Karl-Friedrich Kraiss
    • 1
  1. 1.RWTH Aachen UniversityAachenGermany

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