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Sit-to-stand-and-walk from 120% Knee Height: A Novel Approach to Assess Dynamic Postural Control Independent of Lead-limb
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Predicting Knee Joint Contact Forces During Normal Walking Using Kinematic Inputs With a Long-Short Term Neural

Hunter J Bennett1, Kaileigh Estler2,3, Kevin Valenzuela4

  • 1Neuromechanics Laboratory, Old Dominion University, 1007 Student Recreation Center, Norfolk, VA 23529.

Journal of Biomechanical Engineering
|January 25, 2024
PubMed
Summary

A novel deep learning method accurately predicts knee joint contact forces using simple kinematic data. This approach simplifies force estimation, offering a more accessible tool for clinical applications and research compared to complex modeling.

Keywords:
knee adduction momentknee joint contact forceslong-short term memorymedial compartmentneural network

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Area of Science:

  • Biomechanics
  • Biomedical Engineering
  • Computational Science

Background:

  • Knee joint contact forces are crucial for understanding knee function and pathology.
  • Current estimation methods like external moments and musculoskeletal modeling have limitations in accessibility and clinical applicability.
  • A simplified, accurate method for predicting knee forces is needed.

Purpose of the Study:

  • To develop and validate a novel, simplistic prediction method for knee joint contact forces using minimal kinematic inputs.
  • To compare the accuracy of the novel method against traditional musculoskeletal modeling.

Main Methods:

  • Utilized marker trajectories and instrumented knee forces from the Grand Challenge and CAMS datasets.
  • Derived lower limb and trunk kinematics via inverse kinematics.
  • Developed a long-short term memory (LSTM) network to predict medial and lateral knee forces using kinematic data.
  • Derived forces using OpenSim musculoskeletal modeling for comparison.

Main Results:

  • The LSTM network achieved high accuracy for medial knee forces (R2 = 0.77), requiring only frontal hip/knee and sagittal hip/ankle kinematics.
  • Musculoskeletal modeling also showed good medial force prediction (R2 = 0.77).
  • Both methods demonstrated poor accuracy for lateral knee forces (LSTM R2 = 0.18, Modeling R2 = 0.21).

Conclusions:

  • A deep learning approach using LSTM networks can accurately predict knee joint medial contact forces with simplified kinematic inputs.
  • This method shows promise as a more accessible alternative to complex musculoskeletal modeling for estimating knee joint forces.
  • Further research is needed to improve lateral force prediction accuracy.