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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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Impact of PCA Pre-Normalization Methods on Ground Reaction Force Estimation Accuracy.

Amal Kammoun1,2, Philippe Ravier1, Olivier Buttelli1,3

  • 1PRISME Laboratory, University of Orleans, 12 Rue de Blois, 45100 Orleans, France.

Sensors (Basel, Switzerland)
|February 24, 2024
PubMed
Summary

Choosing the right pre-normalization method is crucial for accurately estimating ground reaction forces (GRF) using principal component analysis with machine learning (PCA-ML). The best method depends entirely on the specific machine learning technique employed.

Keywords:
GRF component estimationPCA pre-normalizationforce plate measurementinsole measurementmachine learningnormalization methods

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Area of Science:

  • Biomechanics
  • Sports Science
  • Biomedical Engineering

Background:

  • Insole pressure sensors can estimate ground reaction force (GRF) components.
  • Principal Component Analysis combined with Machine Learning (PCA-ML) is a common approach for this estimation.
  • PCA requires pre-normalization, and its impact on GRF estimation accuracy is not fully understood.

Purpose of the Study:

  • To evaluate the impact of twelve pre-normalization methods on GRF component estimation accuracy.
  • To compare the performance of three PCA-ML methods (PCA-ANN, PCA-LS, PCA-SVR) with different normalizations.
  • To determine optimal normalization strategies for specific PCA-ML techniques in GRF estimation.

Main Methods:

  • Evaluated twelve pre-normalization methods applied to insole pressure sensor data.
  • Utilized three PCA-ML methods: Artificial Neural Network (ANN), Least Square (LS), and Support Vector Regression (SVR).
  • Assessed accuracy against gold-standard force plate measurements from nine subjects during walking.

Main Results:

  • Normalization method performance varied significantly across different ML techniques.
  • Body weight normalization was optimal for PCA-ANN but worst for PCA-SVR.
  • Vector standardization was recommended for PCA-ANN and PCA-LS, while the mean method was recommended for PCA-SVR.

Conclusions:

  • The choice of pre-normalization method is critically dependent on the selected machine learning algorithm.
  • Selecting a normalization method independently of the ML technique can lead to suboptimal or inaccurate GRF estimations.
  • Specific normalization recommendations are provided for PCA-ANN, PCA-LS, and PCA-SVR to improve GRF estimation accuracy.