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Cross-step detection using center-of-pressure based algorithm for real-time applications.

Matjaž Zadravec1, Zlatko Matjačić2

  • 1University Rehabilitation Institute Republic of Slovenia, Linhartova 51, SI-1000, Ljubljana, Slovenia. matjaz.zadravec@ir-rs.si.

Journal of Neuroengineering and Rehabilitation
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Summary
This summary is machine-generated.

This study introduces a new algorithm for real-time gait event detection using Center of Pressure (CoP) signals from instrumented treadmills. The algorithm accurately identifies gait events and cross-steps, improving practical applications in rehabilitation.

Keywords:
Balance assessmentBiofeedbackCenter of pressureCross-stepGait event detectionInstrumented treadmillPerturbation-based balance trainingReal-time algorithmTreadmill walking

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Area of Science:

  • Biomechanics
  • Rehabilitation Engineering
  • Human Locomotion Analysis

Background:

  • Gait event detection is vital for walking rehabilitation and biofeedback.
  • Current online algorithms often require add-on sensors, limiting practicality.
  • Instrumented treadmills using Center of Pressure (CoP) signals offer a sensor-free alternative but struggle with perturbed walking.

Purpose of the Study:

  • To develop and validate a real-time, CoP-based algorithm for detecting gait events and cross-steps.
  • To address limitations of existing methods in detecting cross-steps during perturbed walking.
  • To evaluate the algorithm's performance across diverse populations and walking conditions.

Main Methods:

  • A novel algorithm combining thresholding and logic techniques was developed.
  • The algorithm was tested on CoP data from healthy individuals, stroke survivors, and individuals with transtibial amputation.
  • Performance was assessed by comparing real-time detected events with offline identified events, measuring absolute mean errors (AME) and success rates.

Main Results:

  • The algorithm achieved high accuracy in detecting both native gait events and cross-step events during perturbed walking.
  • A 94% success rate was observed for cross-step detection, with average AME for heel strikes at 64 ms and toe-offs at 111 ms.
  • Performance was consistent across different populations, though walking speed influenced success rates in healthy participants.

Conclusions:

  • The developed CoP-based algorithm advances real-time gait event detection on instrumented treadmills.
  • It effectively identifies gait events and cross-steps, offering valuable insights into human locomotion.
  • The algorithm's robustness across diverse populations and gait patterns supports its potential in clinical gait analysis and rehabilitation.