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Related Experiment Video

Updated: Dec 3, 2025

Postural Organization of Gait Initiation for Biomechanical Analysis Using Force Platform Recordings
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Centre of Pressure Estimation during Walking Using Only Inertial-Measurement Units and End-To-End Statistical

Janez Podobnik1, David Kraljić1, Matjaž Zadravec2

  • 1Faculty of Electrical Engineering, University of Ljubljana, SI-1000 Ljubljana, Slovenia.

Sensors (Basel, Switzerland)
|October 31, 2020
PubMed
Summary
This summary is machine-generated.

We developed a new method to estimate the center of pressure (COP) during gait analysis using only inertial measurement unit (IMU) data. This approach offers accurate gait assessment for individuals with motor impairments, even with reduced sensor numbers and simplified calibration.

Keywords:
artificial neural networkbalanceestimation of centre of pressuregait analysisgait modelinertial measurement unitrehabilitationwearable sensors

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Area of Science:

  • Biomechanics
  • Rehabilitation Engineering
  • Wearable Technology

Background:

  • Center of pressure (COP) estimation is crucial for gait analysis, particularly in assessing functional capacity in individuals with motor impairments.
  • Traditional methods often rely on force sensors or complex setups.
  • Inertial Measurement Units (IMUs) offer a portable and versatile alternative for capturing gait dynamics.

Purpose of the Study:

  • To present a novel methodology for estimating COP using only raw data from IMUs (gyroscopes, accelerometers, magnetometers).
  • To validate the proposed statistical modeling approach using both linear and Long-Short-Term Memory (LSTM) neural network models.
  • To assess the performance and practical applicability of the IMU-based COP estimation method.

Main Methods:

  • Utilized raw gyroscope, accelerometer, and magnetometer data from IMUs for COP estimation.
  • Developed and trained statistical models, including a linear model and an LSTM neural network.
  • Trained models using ground truth COP data obtained from an instrumented treadmill.
  • Investigated the impact of reduced calibration time, removal of magnetic IMU data, and decreased IMU count on model performance.

Main Results:

  • Achieved an average intra-subject root mean square (RMS) error of 12.3 mm and an average inter-subject RMS error of 23.7 mm, comparable or superior to existing studies.
  • Demonstrated that a short calibration procedure (a few minutes) does not significantly decrease model performance.
  • Showed that the magnetic component of IMU data can be omitted without substantial performance degradation.
  • Confirmed that reducing the number of IMUs to five does not compromise model accuracy.

Conclusions:

  • The proposed methodology enables accurate COP estimation using solely IMU data, offering a practical solution for gait analysis.
  • The method is robust to reduced calibration times and can function effectively with fewer IMUs and without magnetic data.
  • This approach holds significant potential for remote and accessible gait assessment, especially for individuals with motor impairments.