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Related Experiment Video

Updated: Oct 30, 2025

Clinical-oriented Three-dimensional Gait Analysis Method for Evaluating Gait Disorder
06:54

Clinical-oriented Three-dimensional Gait Analysis Method for Evaluating Gait Disorder

Published on: March 4, 2018

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A Fully Wireless Wearable Motion Tracking System with 3D Human Model for Gait Analysis.

Kevin Lee1, Wei Tang2

  • 1Engineering Physics and Electrical Engineering, New Mexico State University Main Campus, 1125 Frenger Mall, Las Cruces, NM 88003, USA.

Sensors (Basel, Switzerland)
|July 2, 2021
PubMed
Summary

This affordable wearable motion tracking system aids gait analysis and interlimb coordination. It offers recording and playback for rehabilitation, fall risk reduction, and identifying deviant gait patterns.

Keywords:
IMUgait analysisinterlimb coordinationmotion trackingwearable devices

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Last Updated: Oct 30, 2025

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Area of Science:

  • Biomechanics
  • Rehabilitation Engineering
  • Wearable Technology

Background:

  • Gait analysis is crucial for understanding movement disorders and informing rehabilitation strategies.
  • Existing motion tracking systems can be expensive, cumbersome, or limited to specific environments.
  • Accurate and accessible tools are needed to assess gait and interlimb coordination in clinical and research settings.

Purpose of the Study:

  • To present a novel, affordable, and convenient wearable motion tracking system.
  • To enable recording and playback functionalities for comprehensive gait analysis.
  • To support applications in fall risk reduction and gait retraining.

Main Methods:

  • Development of a system comprising ten custom wearable straps, a receiver, and a central computer.
  • Implementation of motion tracking capabilities for both lower and upper body segments.
  • Calculation of five key gait parameters during experimental trials.

Main Results:

  • The system successfully calculates five gait parameters, demonstrating its utility in quantitative gait assessment.
  • The system has the potential to identify deviant gait patterns by analyzing kinematic data.
  • Tracking of upper body parameters, such as arm swing, was achieved, offering insights into interlimb coordination.

Conclusions:

  • The proposed wearable motion tracking system offers an affordable and versatile solution for gait analysis and interlimb coordination studies.
  • Its recording and playback features are valuable for rehabilitation settings, aiding in fall risk assessment and gait retraining.
  • The system's ability to track both lower and upper body movements holds promise for the study of pathological gaits and complex movement patterns.