Tracking Knee Joint Functional Axes through Tikhonov Filtering and Plűcker Coordinates

Area of Science:

• Biomechanics
• Orthopedics
• Medical Engineering

Background:

• Estimating bone and joint position from skin markers is challenging due to Soft Tissue Artifacts (STA).
• Instantaneous Screw Axes (ISA) are used to simplify rigid body motion, with Knee Functional Axes (KFA) being specific to the knee.
• ISA estimations are sensitive to noisy data, necessitating regularization techniques like Tikhonov Regularization Filtering (TRF).

Purpose of the Study:

• To establish a reciprocal connection between Knee Functional Axes (KFA) and Ground Reaction Forces (GRF).
• To utilize this connection for estimating knee joint constraint forces.
• To evaluate computational performance against existing kinetic and kinematic data from instrumented knee replacements.

Main Methods:

• Application of Tikhonov Regularization Filtering (TRF) for estimating Instantaneous Screw Axes (ISA).
• Establishing a reciprocal relationship between KFA and Ground Reaction Forces (GRF).
• Comparison of computational results with published data from instrumented total knee replacements.

Main Results:

• Demonstrated a reciprocal connection between KFA and GRF for estimating joint constraint forces.
• Presented computational performance comparable to existing methods using instrumented knee replacement data.
• Preliminary findings suggest implications for dynamic alignment as a functional anatomic metric.

Conclusions:

• The study successfully established a novel method linking KFA and GRF for joint constraint force estimation.
• The findings support the potential of dynamic alignment as a functional anatomic metric.
• This approach offers a promising avenue for reducing the impact of Soft Tissue Artifacts (STA) in biomechanical analysis.