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Improved running gait parameter estimation from single foot-mounted IMU data based on refined event detection.

Yiwei Wu1, Haoran Zhang2, Shuhan Wang1

  • 1School of Sport Science, Beijing Sport University, Beijing, China.

Frontiers in Bioengineering and Biotechnology
|January 29, 2026
PubMed
Summary

A new method using inertial measurement units (IMUs) improves running gait analysis by fusing sensor data for precise event detection. This approach enhances accuracy for spatial and temporal gait parameters compared to conventional methods.

Keywords:
gait event detectioninertial measurement unitsrunning gait analysisvalidationzero-velocity update

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Area of Science:

  • Biomechanics
  • Wearable Technology
  • Sports Science

Background:

  • Accurate gait analysis is crucial for portable monitoring using inertial measurement units (IMUs).
  • Conventional IMU algorithms struggle with running gait due to speed variations and diverse foot-strike patterns, necessitating adaptive strategies.
  • High precision running gait analysis requires improved event detection methods.

Purpose of the Study:

  • To introduce MFD-GED (multi-sensor fusion with dynamic gait event detection), a novel method for precise running gait analysis using a single foot-mounted IMU.
  • To enhance gait event detection (initial contact, terminal contact, mid-stance) by fusing acceleration and angular velocity data.
  • To compute comprehensive running biomechanics parameters and assess the method's validity and performance improvements.

Main Methods:

  • The MFD-GED framework fuses acceleration and angular velocity features from a foot-mounted IMU.
  • A parametric strategy identifies key gait events: initial contact (IC), terminal contact (TC), and mid-stance (MS).
  • The method was validated against a laboratory reference system (optical motion capture, force plates) using correlation coefficients, Bland-Altman analysis, and paired t-tests.

Main Results:

  • MFD-GED demonstrated high concurrent validity with the laboratory reference system (Pearson's r = 0.743-0.991, ICC = 0.741-0.990).
  • Compared to the angular-velocity-based gait-segmentation (AVGS) method, MFD-GED significantly reduced bias in spatial parameters (e.g., stride velocity, stride length) and temporal parameters (e.g., contact time, flight time).
  • Bias in peak vertical ground reaction force (vGRF) also decreased, with reduced error standard deviations across all measured metrics.

Conclusions:

  • The MFD-GED framework effectively improves running gait detection and enables high-fidelity parameter estimation using IMUs.
  • The method shows significant potential for future gait monitoring applications, offering a reliable tool for professionals.
  • While validated in healthy young males, the findings support its utility for advanced running gait analysis.