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

Original Article


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.


Knee kinematics Screw axis Pathology classification Support vector machines 



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


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Copyright information

© International Federation for Medical and Biological Engineering 2007

Authors and Affiliations

  1. 1.Faculty of Mechanical EngineeringTechnion, Israel Institute of TechnologyHaifaIsrael
  2. 2.The Robotics InstituteCarnegie Mellon UniversityPittsburghUSA

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