Multimedia Tools and Applications

, Volume 76, Issue 5, pp 6189–6208 | Cite as

An information-theoretic treatment of passive haptic media

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Abstract

Haptic rendering has been long considered as the process of estimating the force that stems from the interaction of a user and an object. Even if this approach follows the principles of natural haptic interaction, it places severe limitations in processing haptic media. This paper presents an information theoretic framework that aims to provide a new view of haptic rendering that can accommodate for open-loop synthetic haptic media, where interaction-based rendering is a special case. As a result, using the proposed information-theoretic approach, the haptic signal can be precomputed as a force field, stored and then filtered by taking into account device and perceptual capabilities of the receiver in order to lower the required bandwidth of the resulting stream, thus opening new possibilities for the representation and processing of haptic media.

Keywords

Haptic rendering Information theory Haptic information loss Haptic filter Haptic coding 

References

  1. 1.
    Borst C (2005) Predictive coding for efficient host-device communication in a pneumatic force-feedback display. In: Proceedings first joint eurohaptics conference symposium. Haptic interfaces for virtual environ. Teleoperator system, pisa, Italy pp 596–599Google Scholar
  2. 2.
    Cha J, Ho YS, Kim Y, Ryu J (2009) A framework for haptic broadcasting. IEEE Multimedia 16(3):16–27CrossRefGoogle Scholar
  3. 3.
    El-Saddik A, Orozco M, Asfaw Y, Shirmohammadi S, Adler A (2007) A novel biometric system for identification and verification of haptic users. IEEE Trans Instrum Meas 56(3):895–906. doi:10.1109/TIM.2006.887174 CrossRefGoogle Scholar
  4. 4.
    Guruswamy V, Lang J, Lee WS (2011) Iir filter models of haptic vibration textures. IEEE Trans Instrum Meas 60(1):93–103. doi:10.1109/TIM.2010.2065751 CrossRefGoogle Scholar
  5. 5.
    Hamam A, Saddik A (2013) Toward a mathematical model for quality of experience evaluation of haptic applications. IEEE Trans Instrum Meas PP(99):1–1. doi:10.1109/TIM.2013.2272859 Google Scholar
  6. 6.
    Hayward V (2011) Is there a plenhaptic function. Phil Trans R Soc B 366:3115–3122CrossRefGoogle Scholar
  7. 7.
    Hinterseer P, Hirche S, Chaudhuri S, Steinbach E, Buss M (2008) Perception-based data reduction and transmission of haptic data in telepresence and teleaction systems. IEEE Trans Signal Process 56(2):588–597MathSciNetCrossRefGoogle Scholar
  8. 8.
    Hirche S, Bauer A, Buss M (2005) Transparency of haptic telepresence systems with constant time delay. In: Proceeding IEEE International Conference Control Appl., Toronto, Canada pp. 328–333Google Scholar
  9. 9.
    Hossain S, Rahman A, El-Saddik A (2011) Measurements of multimodal approach to haptic interaction in second life interpersonal communication system. IEEE Trans Instrum Meas 60(11):3547–3558. doi:10.1109/TIM.2011.2161148 CrossRefGoogle Scholar
  10. 10.
    Jianxiong X, Owens A, Torralba A (2013). SUN3D: A Database of Big Spaces Reconstructed Using SfM and Object Labels. In: 2013 IEEE International Conference on Computer Vision (ICCV), pp 1625–1632Google Scholar
  11. 11.
    Kim M, Lee S, Choi S (2014) Saliency-driven real-time video-to-tactile translation. IEEE Transactions on Haptics 7(3):394–404CrossRefGoogle Scholar
  12. 12.
    Kostopoulos K, Moustakas K, Tzovaras D, Nikolakis G, Thillou C, Gosselin B (2007) Haptic access to conventional 2d maps for the visually impaired. Springer International Journal on Multimodal User Interfaces 1(2):13–19CrossRefGoogle Scholar
  13. 13.
    Kron A, Schmidt G, Petzold B, Zh MF, Hinterseer P, Steinbach E (2004) Disposal of explosive ordnances by use of a bimanual haptic telepresence system. In: Proceedings IEEE International Conference Robot. Autom., New Orleans, LA pp 1968–1973Google Scholar
  14. 14.
    Kuschel M, Cremer P, Buss M (2009) Passive haptic data-compression methods with perceptual coding for bilateral presence systems. IEEE Trans Syst Man Cybern Syst Hum 39(6):1142– 1151CrossRefGoogle Scholar
  15. 15.
    Laycock S, Day A (2007) A survey of haptic rendering techniques. Comput Graphics Forum 26(1):50–65CrossRefGoogle Scholar
  16. 16.
    Lin M, Otaduy M (2008) Haptic rendering: foundations, algorithms and applications. p A.K.Peters publishingGoogle Scholar
  17. 17.
    Lloyd AS (2006) Least squares quantization in PCM. IEEE Trans Inf Theor 28(2):129–137. doi:10.1109/TIT.1982.1056489 MathSciNetCrossRefMATHGoogle Scholar
  18. 18.
    Moustakas K (2013) Haptic media from an information theoretic perspective. In: IEEE International symposium on haptic, audiovisual environments and games, IEEE HAVE 2013, IstanbulGoogle Scholar
  19. 19.
    Moustakas K, Nikolakis G, Tzovaras D, Strintzis M (2005) Stereoscopic video generation based on efficient layered structure and motion estimation from a monoscopic image sequence. IEEE Trans Circuits Syst Video Technol 15(8):1065–1073CrossRefGoogle Scholar
  20. 20.
    Moustakas K, Tzovaras D, Strintzis M (2007) Sq-map: Efficient layered collision detection and haptic rendering. IEEE Trans Vis Comput Graph 13(1):80–93CrossRefGoogle Scholar
  21. 21.
    Moustakas K, Nikolakis G, Kostopoulos K, Tzovaras D, Strintzis M (2007) Haptic rendering of visual data for the visually impaired. IEEE Multimedia 14(1):62–72CrossRefGoogle Scholar
  22. 22.
    Nikolakis G, Koutsonanos D, Daras P, Moustakas K, Tzovaras D, Strintzis M (2006) Haptic interaction in medical virtual environments. In: Wiley Encyclopedia of Biomedical Engineering, 6- Volume Set, Metin Akay (Editor) ISBN : 0-471-24967-XGoogle Scholar
  23. 23.
    Ortega A, Liu Y (2002) Lossy compression of haptic data. In: Touch in virtual environments: Haptics and the design of interactive systems. Englewood cliffs, NJ: Prentice-hall, ch. 6, pp 119–136Google Scholar
  24. 24.
    Ou E, Basdogan C (p 2002) Network considerations for a dynamic shared haptic environment. In: Proceedings Nat. Conf. Undergrad. Res Whitewater, WIGoogle Scholar
  25. 25.
    Robles-De-La-Torre G (2006) The importance of the sense of touch in virtual and real environments. IEEE Multimedia 13(3):24–30CrossRefGoogle Scholar
  26. 26.
    Saddik E (2007) The potential of haptics technologies. IEEE Instrum Meas Mag 10(1):10–17CrossRefGoogle Scholar
  27. 27.
    Sakr N, Georganas ND, Zhao J (2011) Human perception-based data reduction for haptic communication in six-dof telepresence systems. IEEE Trans Instrum Meas 60(11):3534–3546. doi:10.1109/TIM.2011.2161144 CrossRefGoogle Scholar
  28. 28.
    Shen X, Zhou J, El-Saddik A, Georganas ND (2004) Architecture and evaluation of tele-haptic environments. In: Eighth IEEE international symposium on distributed simulation and real-time applications, DS-RT, pp 53–60Google Scholar
  29. 29.
    Sakr N, Zhou J, Georganas ND, Zhao J (2009) Prediction-based haptic data reduction and transmission in telementoring systems. IEEE Trans Instrum Meas 58(5):1727–1736. doi:10.1109/TIM.2008.2009146 CrossRefGoogle Scholar
  30. 30.
    Srinivasan M, Basdogan C (1997) Haptics in virtual environments: taxonomy, research status and challenges. Comput Graph:393–404Google Scholar
  31. 31.
    Ternes D, MacLean K (2008) Designing large sets of haptic icons with rhythm. Springer LNCS Haptics: Perception Devices and Scenarios 5024:199–208Google Scholar
  32. 32.
    Vu MH, Na UJ (2011) A new 6-dof haptic device for teleoperation of 6-dof serial robots. IEEE Trans Instrum Meas 60(11):3510–3523. doi:10.1109/TIM.2011.2164285 CrossRefGoogle Scholar
  33. 33.
    Wang H, Liu X (2011) Haptic interaction for mobile assistive robots. IEEE Trans Instrum Meas 60(11):3501–3509. doi:10.1109/TIM.2011.2161141 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Electrical and Computer Engineering DepartmentUniversity of PatrasRion-PatrasGreece

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