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Tekscan analysis programs (TAP) for quantifying dynamic contact mechanics.

Tony Chen1, Ashley Pekmezian2, Erin R Leatherman3

  • 1Department of Biomechanics and Hospital for Special Surgery, New York, NY 10021 USA; Orthopedic Soft Tissue Research Program, Hospital for Special Surgery, New York, NY 10021 USA.

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|April 12, 2022
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Summary
This summary is machine-generated.

Customized Tekscan Analysis Programs (TAP) extract joint contact mechanics from piezoelectric sensors. Analysis of observer variability in identifying regions of interest (ROI) ensures accurate knee contact mechanics quantification.

Keywords:
Cadaveric testingContact mechanicsMATLABSensorTekscan

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Area of Science:

  • Biomechanics
  • Biomedical Engineering
  • Orthopedics

Background:

  • Accurate quantification of knee contact mechanics is crucial for understanding joint function and pathology.
  • Piezoelectric sensors provide valuable data, but analysis of this data, particularly identifying regions of interest (ROI), can be subject to user variability.
  • Standardized and validated methods are needed to ensure reliable and reproducible analysis of contact mechanics data.

Purpose of the Study:

  • To present customized Tekscan Analysis Programs (TAP) for extracting comprehensive contact mechanics metrics from piezoelectric sensors in articulating joints.
  • To quantify the variability in identifying ROIs among different users and compare it to an expert.
  • To investigate the influence of knee specimens, gait cycle points, averaging methods, and observers on the accuracy of ROI quantification.

Main Methods:

  • Development of customized Tekscan Analysis Programs (TAP) to process piezoelectric sensor data from articulating joints.
  • Quantification of inter-observer variability in identifying regions of interest (ROI) across seven users and an expert.
  • Application of a multivariate linear mixed effects model to analyze the relative error in force ratio based on four variables: knee specimens, gait cycle points, averaging methods, and observers.
  • Calculation of relative error between observer-defined ROIs and expert-defined ROIs.

Main Results:

  • Statistically significant differences in mean relative errors were observed at different gait cycle points (p < 0.05).
  • Variability in relative errors was quantified and attributed to observer, knee specimen, and random error.
  • The study identified key factors contributing to variability in ROI segmentation for knee contact mechanics analysis.

Conclusions:

  • The developed TAP and accompanying training materials aim to reduce inter-observer variability and improve the accuracy of knee contact mechanics segmentation.
  • Sharing the code and manual facilitates broader application and modification for diverse sensor, joint, and test condition analyses.
  • This work contributes to more reliable and reproducible biomechanical analyses of joint contact mechanics.