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A method for gait events detection based on low spatial resolution pressure insoles data.

F Salis1, S Bertuletti1, T Bonci2

  • 1Department of Biomedical Sciences, University of Sassari, Sassari, Italy; Interuniversity Centre of Bioengineering of the Human Neuromusculoskeletal System, Sassari, Italy.

Journal of Biomechanics
|August 29, 2021
PubMed
Summary

This study introduces a new method for accurately identifying foot contacts using instrumented insoles, overcoming limitations of traditional gait analysis tools. The validated approach enables reliable spatio-temporal parameter estimation for improved gait analysis.

Keywords:
Gait analysisGait eventsLocomotionPressure insolesWearable sensors

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Area of Science:

  • Biomechanics
  • Gait Analysis
  • Wearable Technology

Background:

  • Accurate identification of initial and final foot contacts is essential for reliable gait analysis.
  • Current gold standard methods (force platforms, instrumented walkways) are expensive, non-portable, and lab-bound.
  • Instrumented insoles offer a portable alternative but lack standardized gait event identification methods.

Purpose of the Study:

  • To present and validate a novel method for identifying gait events using instrumented insoles.
  • To overcome the limitations of existing laboratory-based gait analysis techniques.
  • To enable portable and accessible gait analysis.

Main Methods:

  • Utilized two insoles, each with 16 pressure sensing elements (310 mm² area) sampling at 100 Hz.
  • Employed sensor redundancy and a cluster-based strategy for gait event identification.
  • Validated the method against force platforms in laboratory settings with nine healthy subjects (801 contacts).

Main Results:

  • Achieved low average errors in detecting initial foot contacts (approx. 20 ms) and final foot contacts (approx. 10 ms).
  • Demonstrated low average errors in estimating stance duration (approx. 20 ms) and step duration (<10 ms).
  • The method's applicability to other pressure insoles with similar specifications was confirmed.

Conclusions:

  • The presented cluster-based method effectively identifies gait events using instrumented insoles.
  • This approach offers a viable, portable, and accurate alternative for spatio-temporal gait parameter estimation.
  • The method's adaptability suggests potential for widespread use in clinical and research settings.