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  3. Ground Reaction Force Estimation Via Time-aware Knowledge Distillation.
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Ground Reaction Force Estimation via Time-aware Knowledge Distillation.

Eun Som Jeon1, Sinjini Mitra2, Jisoo Lee2

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

IEEE Internet of Things Journal
|September 8, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

We developed a Time-aware Knowledge Distillation framework to accurately estimate ground reaction forces (GRF) using wearable insole sensors. This efficient method improves human gait analysis for portable healthcare applications.

Keywords:
Ground reaction forceinsole sensorknowledge distillationsensor data estimationwearable sensor data

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Area of Science:

  • Biomechanics
  • Wearable Technology
  • Machine Learning

Background:

  • Human gait analysis is crucial for healthcare, rehabilitation, and diagnostics.
  • Instrumented treadmills are accurate but costly and non-portable for measuring ground reaction forces (GRF).
  • Wearable insole sensors offer portability but face accuracy and noise challenges, while deep learning solutions are resource-intensive.

Purpose of the Study:

  • To propose an efficient and accurate method for estimating GRF from wearable insole sensor data.
  • To address the limitations of current GRF measurement techniques for portable gait analysis.
  • To develop a computationally inexpensive deep learning framework suitable for real-time applications.

Main Methods:

  • Developed a Time-aware Knowledge Distillation framework to leverage temporal and feature similarity.
  • Utilized lightweight 'student' models trained via distillation from more complex 'teacher' models.
  • Evaluated performance against instrumented treadmill GRF measurements across various walking speeds and model architectures.

    • Main Results:

    • The proposed Time-aware Knowledge Distillation framework significantly outperformed existing baselines in GRF estimation accuracy.
    • Distilled lightweight models achieved excellent accuracy and reliability comparable to more complex methods.
    • The framework demonstrated a substantial reduction in required training parameters, enhancing efficiency.

    Conclusions:

    • Time-aware Knowledge Distillation provides a data- and resource-efficient solution for accurate GRF estimation from wearable sensors.
    • This approach enables practical, real-time human gait analysis in portable and diverse settings.
    • The framework enhances the feasibility of wearable sensor technology for widespread healthcare and rehabilitation applications.