Skip to main content
SpringerLink
Log in

Recognizing knee pathologies by classifying instantaneous screws of the six degrees-of-freedom knee motion

  • Original Article
  • Published:
Medical & Biological Engineering & Computing Aims and scope Submit manuscript

Abstract

We address the problem of knee pathology assessment by using screw theory to describe the knee motion and by using the screw representation of the motion as an input to a machine learning classifier. The flexions of knees with different pathologies are tracked using an optical tracking system. The instantaneous screw parameters which describe the transformation of the tibia with respect to the femur in each two successive observation is represented as the instantaneous screw axis of the motion given in its Plücker line coordinates along with its corresponding pitch. The set of instantaneous screw parameters associated with a particular knee with a given pathology is then identified and clustered in R 6 to form a “signature” of the motion for the given pathology. Sawbones model and two cadaver knees with different pathologies were tracked, and the resulting screws were used to train a classifier system. The system was then tested successfully with new, never-trained-before data. The classifier demonstrated a very high success rate in identifying the knee pathology.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

Notes

  1. For femur: mechanical axis refers to the line drawn from the center of the femoral head to the medial tibial spine; for tibia: mechanical axis refers to the angle formed by a line drawn from the medial tibial spine and the center of the ankle joint.

  2. See discussion on registration and registration free procedure in the conclusion

References

  1. Andriacchi T P, Alexander E J., et al. (1998) A point cluster method for in vivo motion analysis: applied to a study of knee kinematics. J Biomech Eng 120(6):743–749

    Google Scholar 

  2. Ball RS (1900) A treatise on the theory of screws. Cambridge University Press Cambridge

    Google Scholar 

  3. Begg R, Kamruzzaman J (2005) A machine learning approach for automated recognition of movement patterns using basic, kinetic and kinematic gait data. J Biomech 38:401–408

    Article  Google Scholar 

  4. Blankevoort L, Huiskes R, et al. (1990) Helical axes of passive knee joint motions. J Biomech 23(12):1219

    Article  Google Scholar 

  5. Bottlang M, Marsh JL, et al. (1998) Factors influencing accuracy of screw displacement axis detection with a D.C.-based electromagnetic tracking system. J Biomech Eng Trans ASME 120(3):431

    Google Scholar 

  6. Burges CJC (1998) A tutorial on support vector machines for pattern recognition. Data Mining Knowl Disc 2(2):121–167

    Article  Google Scholar 

  7. Caruntu DI, Hefzy MS (2004) 3-D anatomically based dynamic modeling of the human knee to include tibio-femoral and patello-femoral joints. J Biomech Eng 126(1):44–53

    Article  Google Scholar 

  8. Davidson JK, Hunt KH (2004) Robots and screw theory: applications of kinematics and statics to robotics. Oxford University Press, New York

    MATH  Google Scholar 

  9. DiGioia AB, Jaramaz J, et al. (2000) Surgical navigation for total hip replacement with the use of Hipnav. Oper Techn Orthop 10(1):3–8

    Article  Google Scholar 

  10. Duck TR, Ferreira LM, et al. (2004) Assessment of screw displacement axis accuracy and repeatability for joint kinematic description using an electromagnetic tracking device. J Biomech 37(1):163

    Article  Google Scholar 

  11. Fernandez JW, Hunter P J (2005) An anatomically based patient-specific finite element model of patella articulation: towards a diagnostic tool. Biomech Model Mechanobiol 4(1):20–38

    Article  Google Scholar 

  12. Hart R, Mote C J, et al. (1991) A finite helical axis as a landmark for kinematic reference of the knee. J Biomech Eng 113(2):215–222

    Google Scholar 

  13. Hasan SS, Hurwitz DE, et al. (1998) Dynamic evaluation of knee instability during gait in anterior cruciate ligament deficient patients. Trans Orthop Res Soc 44:805

    Google Scholar 

  14. Hefzy MS, Ebraheim N, Mekhail A, Caruntu D, Lin H, Yeasting R, (2003) Kinematics of the human pelvis following open book injury. Med Eng Phys 25(4):259–274

    Article  Google Scholar 

  15. Hunt KH (1978) Kinematic geometry of mechanisms. Clarendon, Oxford

    MATH  Google Scholar 

  16. Jonsson H, Karrholm J (1994) Three-dimensional knee joint movements during a step-up: evaluation after anterior cruciate ligament rupture. J Orthop Res 12(6):769–779

    Article  Google Scholar 

  17. Maki B (1997) Gait changes in older adults: predictors of falls or indicators of fear? J Am Geriatr Soc 45:313–320

    Google Scholar 

  18. Pottmann H, Wallner J (2001) Computational line geometry. Springer, Berlin

    MATH  Google Scholar 

  19. Roth B (1984) Screws, motors, and wrenches that cannot be bought in a hardware store. MIT Press, Cambridge

    Google Scholar 

  20. Scholten R J, Opstelten W, et al. (2003) Accuracy of physical diagnostic tests for assessing ruptures of the anterior cruciate ligament: a meta-analysis. J Fam Pract

  21. Shiavi R, Limbird T, et al. (1987) Helical motion analysis of the knee—I. Methodology for studying kinematics during locomotion. J Biomech 20:459–469

    Article  Google Scholar 

  22. Soudan K, Van Audekercke R, Martens M (1979) Methods, difficulties and inaccuracies in the study of human joint kinematics and pathokinematics by the instant axis concept. Example: the knee joint. J Biomech 12:27–33

    Article  Google Scholar 

  23. Vapnik VN (1995) The nature of statistical learning theory. Springer, New York

    MATH  Google Scholar 

  24. Vapnik VN (1998) Statistical learning theory. Wiley, New York

    MATH  Google Scholar 

  25. Woltring HJ, Huiskes R, et al. (1985) Finite centroid and helical axis estimation from noisy landmark measurements in the study of human joint kinematics. J Biomech 185:379–389

    Article  Google Scholar 

Download references

Acknowledgment

This work has been supported by NSF ITR grant IIS-0325920.

Author information

Authors and Affiliations

  1. Faculty of Mechanical Engineering, Technion, Israel Institute of Technology, Technion City, Haifa, 32000, Israel

    Alon Wolf

  2. The Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, 15213, USA

    Amir Degani

Authors
  1. Alon Wolf
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Amir Degani
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alon Wolf.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Wolf, A., Degani, A. Recognizing knee pathologies by classifying instantaneous screws of the six degrees-of-freedom knee motion. Med Bio Eng Comput 45, 475–482 (2007). https://doi.org/10.1007/s11517-007-0174-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-007-0174-1

Keywords

Search

Navigation