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Comparison of the Accuracy of Ground Reaction Force Component Estimation between Supervised Machine Learning and Deep

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Summary

Supervised Machine Learning (SML) methods, specifically Random Forest (RF), accurately estimate Ground Reaction Force (GRF) components using insole sensors, outperforming Deep Learning (DL) methods in static activities.

Keywords:
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Area of Science:

  • Biomechanics
  • Sensor Technology
  • Machine Learning

Background:

  • Estimating Ground Reaction Force (GRF) components is crucial for biomechanical analysis.
  • Pressure insole sensors offer a portable method for GRF estimation.
  • Comparing various machine learning algorithms for GRF estimation is essential for practical applications.

Purpose of the Study:

  • To estimate GRF components (Fx, Fy, Fz) using pressure insole sensors across six activities, including novel static and manual material handling scenarios.
  • To compare the accuracy of six different methods—three Deep Learning (DL) and three Supervised Machine Learning (SML)—for GRF component estimation.
  • To identify the most accurate method for GRF estimation in different activities.

Main Methods:

  • Six methods were evaluated: Artificial Neural Network, Long Short-Term Memory, Convolutional Neural Network (DL), and Least Squares, Support Vector Regression, Random Forest (SML).
  • Data were collected from nine subjects performing six distinct activities.
  • Root Mean Square Error (RMSE) was used to quantify estimation accuracy against force plate data.

Main Results:

  • The Random Forest (RF) method demonstrated the highest accuracy in estimating GRF components for static activities.
  • RF achieved mean RMSE values significantly lower than reference measurements for static situations.
  • Supervised Machine Learning methods, particularly RF, outperformed the tested Deep Learning methods in GRF estimation accuracy.

Conclusions:

  • Pressure insole sensors coupled with Supervised Machine Learning, specifically Random Forest, provide accurate GRF component estimation.
  • The study expands GRF estimation to new activities, offering valuable insights for biomechanics and ergonomics.
  • RF presents a superior alternative to Deep Learning methods for GRF estimation in static and potentially other activities.