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A Determination Method for Gait Event Based on Acceleration Sensors.

Chang Mei1, Farong Gao1, Ying Li1

  • 1Artificial Intelligence Institute, Hangzhou Dianzi University, Hangzhou 310018, China.

Sensors (Basel, Switzerland)
|December 18, 2019
PubMed
Summary

This study introduces a novel method using inertial sensors to accurately identify gait events, overcoming limitations of expensive motion capture systems for motor dysfunction assessment and rehabilitation.

Keywords:
acceleration signalevent determinationfrequency domain integrationgait recognitioninertial sensorsthreshold segmentation

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Area of Science:

  • Biomechanics
  • Rehabilitation Engineering
  • Sensor Technology

Background:

  • Three-dimensional motion capture (3D Mo-Cap) systems are costly and require specialized environments, limiting clinical use for gait analysis.
  • Inertial sensors offer a cost-effective and portable alternative for collecting human gait data.
  • Acceleration signals from inertial sensors are valuable for human gait recognition.

Purpose of the Study:

  • To develop and validate a method for accurate gait event detection using inertial sensor data.
  • To enable widespread clinical application of gait analysis by overcoming the limitations of traditional motion capture systems.

Main Methods:

  • Utilized wavelet denoising on acceleration signals from heel and toe sensors.
  • Applied a comprehensive change rate threshold for initial signal segmentation.
  • Calculated vertical displacement by double integration of vertical acceleration signals.
  • Identified four key gait events based on vertical displacement characteristics.

Main Results:

  • The proposed method successfully segmented gait signals and identified distinct gait events.
  • Detected gait events demonstrated high consistency with synchronous data from a 3D Mo-Cap system.
  • Achieved accurate subdivision and definition of gait events.

Conclusions:

  • This inertial sensor-based approach provides an accurate and accessible method for gait event detection.
  • The findings support the use of inertial sensors for gait analysis in clinical settings and rehabilitation.
  • The study offers a valuable reference for advancing research in gait recognition technology.