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Related Experiment Video

Updated: Sep 16, 2025

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Predicting Running Vertical Ground Reaction Forces Using Neural Network Models Based on an IMU Sensor.

Shangxiao Li1, Jiahui Pan1, Dongmei Wang2

  • 1Research Center for Sports Psychology and Biomechanics, China Institute of Sport Science, Beijing 100061, China.

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|July 12, 2025
PubMed
Summary
This summary is machine-generated.

This study developed a synchronization method and artificial neural network (ANN) models to predict vertical ground reaction force (vGRF) during running using inertial measurement unit (IMU) data. The models accurately predicted vGRF, offering potential for personalized lower limb load monitoring.

Keywords:
artificial neural networkrunning-related injuriessynchronization algorithmwearable sensor

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Area of Science:

  • Biomechanics of running
  • Sports injury prevention
  • Wearable sensor technology

Background:

  • Vertical ground reaction force (vGRF) is crucial for understanding running-related injuries (RRIs).
  • Accurate vGRF measurement typically requires instrumented treadmills or force plates.
  • Inertial measurement units (IMUs) offer a portable alternative but require synchronization with vGRF data.

Purpose of the Study:

  • To develop and validate a synchronization method (STWS) for IMU and vGRF data during running.
  • To create and assess artificial neural network (ANN) models (WNN and FFNN) for predicting vGRF from IMU acceleration data.
  • To evaluate the accuracy of predicting peak vGRF and overall vGRF curves.

Main Methods:

  • Fifteen runners (rearfoot and forefoot strikers) ran at 12, 14, and 16 km/h.
  • A single IMU and an instrumented treadmill collected acceleration and vGRF data.
  • The sliding time window synchronization (STWS) algorithm was used to align IMU and vGRF data. Wavelet neural network (WNN) and feed-forward neural network (FFNN) models predicted vGRF using acceleration data from the stance phase.

Main Results:

  • The STWS algorithm achieved stride time synchronization with mean absolute errors < 11.2 ms.
  • ANN models showed high correlation (r > 0.97) and agreement (NRMSE 4.6–9.2%, R² 0.93–0.99) between measured and predicted vGRF curves.
  • Peak vGRF prediction had NRMSEs of 1.6–8.2%, with sagittal-axis acceleration data showing good accuracy and reduced input.

Conclusions:

  • The STWS algorithm effectively synchronizes IMU and force plate data during running.
  • Both WNN and FFNN models accurately predict vGRF, with potential for using sagittal-axis acceleration alone.
  • This research provides a foundation for developing personalized ANN models for monitoring lower limb load in runners.