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Carnegie Mellon Robotics Institute

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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 screw parameters which describe the transformation of the tibia with respect to the femur in each two successive observation are represented as the instantaneous screw axis of the motion given in its Pl?ker line coordinate, along with its corresponding pitch. The set of screw parameters associated with a particular knee with a given pathology is then identified and clustered in R6 to form a ?signature? of the motion for the given pathology. Bone 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.

Keywords

Knee Kinematic, Screw Axis, Pathology Classification, Support Vector Machines

Notes

Number of pages: 6

Text Reference

Alon Wolf and Amir Degani, "Classifying Knee Pathologies using Instantaneous Screws of the Six Degrees-of-Freedom Knee Motion," Proceedings of the 2006 IEEE International Conference on Robotics and Automation, May, 2006, pp. 2946-2951.

BibTeX Reference

@inproceedings{Wolf_2006_5447,
   author = "Alon Wolf and Amir Degani",
   title = "Classifying Knee Pathologies using Instantaneous Screws of the Six Degrees-of-Freedom Knee Motion",
   booktitle = "Proceedings of the 2006 IEEE International Conference on Robotics and Automation",
   pages = "2946-2951",
   publisher = "IEEE",
   month = "May",
   year = "2006",
}

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