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Adaptive method for real-time gait phase detection based on ground contact forces.

Lie Yu1, Jianbin Zheng1, Yang Wang1

  • 1School of Information Engineering, Wuhan University of Technology, Wuhan, China; Key Laboratory of Fiber Optic Sensing Technology and Information Processing, Ministry of Education, Wuhan University of Technology, Wuhan, China.

Gait & Posture
|December 4, 2014
PubMed
Summary

A new proportion method (PM) accurately detects real-time gait phases using ground contact forces (GCFs). This adaptive approach offers reliable and real-time gait analysis across various walking conditions.

Keywords:
Force sensitive resistorsGait phase detection algorithmGround contact forcesProportion methodThreshold method

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Area of Science:

  • Biomechanics
  • Wearable Sensors
  • Signal Processing

Background:

  • Traditional threshold methods (TM) for gait phase detection using ground contact forces (GCFs) are not adaptive or real-time.
  • TM requires pre-processing to determine thresholds based on body weight or GCF extremes, limiting its applicability.

Purpose of the Study:

  • To introduce a novel, adaptive, and real-time gait phase detection algorithm (GPDA).
  • To propose a proportion method (PM) for gait phase detection using force sensitive resistors (FSRs).
  • To validate the reliability and adaptability of the PM compared to traditional methods.

Main Methods:

  • Utilizing ground contact forces (GCFs) measured by force sensitive resistors (FSRs).
  • Implementing a proportion method (PM) that calculates sums and proportions of GCFs.
  • Developing a gait phase detection algorithm (GPDA) based on the PM.
  • Comparing PM results against the traditional threshold method (TM).

Main Results:

  • The proposed PM demonstrates high reliability across all tested walking conditions.
  • PM enables real-time gait phase analysis.
  • PM exhibits strong adaptability to varying walking conditions.

Conclusions:

  • The proportion method (PM) is a reliable and adaptive alternative for real-time gait phase detection.
  • PM overcomes the limitations of traditional threshold methods in gait analysis.
  • This novel approach enhances the potential for real-time gait monitoring and analysis.