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Updated: Jun 6, 2026

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior
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Selective Correlation Based Knowledge Distillation for Ground Reaction Force Estimation.

Eun Som Jeon1, Jisoo Lee2, Huisu Lim1

  • 1Department of Computer Science and Engineering, Seoul National University of Science and Technology, Seoul, 01811, Republic of Korea.

Measurement : Journal of the International Measurement Confederation
|June 5, 2026
PubMed
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This summary is machine-generated.

This study introduces Selective Correlation Based Knowledge Distillation (SCKD) to accurately estimate ground reaction force (GRF) using wearable insole sensors. SCKD offers a reliable, resource-efficient solution for portable human gait analysis.

Area of Science:

  • Biomechanics
  • Sensor Technology
  • Machine Learning

Background:

  • Wearable sensors offer portable human gait analysis, crucial for healthcare and sports.
  • Traditional ground reaction force (GRF) measurement is limited by expensive lab equipment.
  • Insole sensors for GRF estimation suffer from noise and accuracy issues, while deep learning demands high computational resources.

Purpose of the Study:

  • To develop a resource-efficient deep learning method for accurate GRF estimation from wearable insole sensor data.
  • To address the limitations of noise, interference, and computational demands in portable gait analysis.
  • To enhance the interpretability and applicability of deep learning models for real-time GRF analysis.

Main Methods:

  • Proposed Selective Correlation Based Knowledge Distillation (SCKD) framework for GRF estimation.
Keywords:
Ground reaction forceinsole sensorknowledge distillationsensor data estimationwearable sensor data

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  • Utilized selected features considering temporal characteristics for correlation map extraction and knowledge transfer.
  • Examined various teacher-student architectures and training approaches with data from different walking speeds and window sizes.

    • Main Results:

    • SCKD demonstrated superior performance in estimating GRF compared to existing methods.
    • The proposed distillation framework generated compact, accurate deep learning models.
    • Experimental results confirmed the effectiveness of SCKD across different gait conditions.

    Conclusions:

    • SCKD provides a reliable and resource-efficient solution for human gait analysis using wearable insole sensors.
    • The method enhances accuracy and interpretability in GRF estimation.
    • Enables real-time gait analysis on portable devices, advancing healthcare and sports applications.