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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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A Deep Learning Model for 3D Ground Reaction Force Estimation Using Shoes with Three Uniaxial Load Cells.

Junggil Kim1, Hyeon Kang1, Seulgi Lee1

  • 1Department of Biomedical Engineering, Konkuk University, Chungju 27478, Republic of Korea.

Sensors (Basel, Switzerland)
|April 13, 2023
PubMed
Summary

Researchers developed a deep learning model using shoes with load cells to estimate ground reaction forces (GRF). This method offers a practical alternative to force plates for gait analysis and inverse dynamics.

Keywords:
gaitload cellseq2seq LSTMthree-axis ground reaction force estimation

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Area of Science:

  • Biomechanics
  • Wearable Technology
  • Machine Learning

Background:

  • Ground reaction force (GRF) is crucial for biomechanical analyses like inverse dynamics.
  • Traditional GRF measurement using force plates has spatial limitations and is impractical for multi-step gait studies.
  • Developing portable and versatile GRF measurement solutions is essential for advancing gait research.

Purpose of the Study:

  • To develop and validate a deep learning model for estimating three-axis GRF using instrumented shoes.
  • To assess the accuracy and reliability of the proposed shoe-based system compared to force plate measurements.
  • To provide a practical solution for GRF estimation in diverse environments, including outdoor settings.

Main Methods:

  • Development of a deep learning model employing a seq2seq approach with Long Short-Term Memory (LSTM).
  • Instrumentation of shoes with three uniaxial load cells to capture GRF data.
  • Collection of GRF data from 81 participants during walking, with data split for training, validation, and testing.
  • Comparison of estimated GRF with force plate measurements using correlation coefficients and root mean square errors.

Main Results:

  • High correlation coefficients (0.97 vertical, 0.96 anterior-posterior, 0.90 medial-lateral) between estimated and measured GRF.
  • Low root mean square errors (65.12 N vertical, 15.50 N anterior-posterior, 9.83 N medial-lateral).
  • A mid-stance timing error of 5.61% and good agreement for maximum vertical GRF via Bland-Altman analysis.

Conclusions:

  • The proposed system using instrumented shoes and a seq2seq LSTM model accurately estimates 3D GRF.
  • This technology overcomes the limitations of force plates, enabling GRF measurement in various environments and for extended gait studies.
  • The system provides valuable data for inverse dynamic analysis, particularly in real-world and multi-step gait research.