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Related Experiment Video

Updated: May 26, 2026

Home-Based Monitor for Gait and Activity Analysis
07:24

Home-Based Monitor for Gait and Activity Analysis

Published on: August 8, 2019

Real-time gait cycle parameter recognition using a wearable accelerometry system.

Che-Chang Yang1, Yeh-Liang Hsu, Kao-Shang Shih

  • 1Department of Mechanical Engineering, Yuan Ze University, 135 Yuan-Tung Rd, Chung-Li, Tao-Yuan 320, Taiwan. ccyang@saturn.yzu.edu.tw

Sensors (Basel, Switzerland)
|December 14, 2011
PubMed
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This study introduces a wearable system using accelerometry to recognize gait cycle parameters in real-time. The system successfully distinguished between Parkinson

Area of Science:

  • Biomedical Engineering
  • Wearable Technology
  • Gait Analysis

Background:

  • Gait disorders significantly impact mobility and quality of life, particularly in conditions like Parkinson's disease (PD).
  • Accurate, real-time assessment of gait parameters is crucial for diagnosis, monitoring, and rehabilitation.
  • Existing methods for gait analysis can be cumbersome or lack real-time feedback capabilities.

Purpose of the Study:

  • To develop and validate a wearable accelerometry system for real-time gait cycle parameter recognition.
  • To quantify and differentiate gait parameters between individuals with and without mobility impairments (e.g., PD patients).
  • To explore the feasibility of using autocorrelation procedures for real-time gait analysis.

Main Methods:

  • Development of a single, waist-mounted, tri-axial accelerometer system to capture trunk accelerations during walking.
Keywords:
Parkinson’s diseaseaccelerometeraccelerometrygaitmobility

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Clinical Assessment of Spatiotemporal Gait Parameters in Patients and Older Adults
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Clinical Assessment of Spatiotemporal Gait Parameters in Patients and Older Adults

Published on: November 7, 2014

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Last Updated: May 26, 2026

Home-Based Monitor for Gait and Activity Analysis
07:24

Home-Based Monitor for Gait and Activity Analysis

Published on: August 8, 2019

Clinical Assessment of Spatiotemporal Gait Parameters in Patients and Older Adults
08:56

Clinical Assessment of Spatiotemporal Gait Parameters in Patients and Older Adults

Published on: November 7, 2014

  • Real-time estimation of gait cycle parameters (cadence, step/stride regularity, step symmetry) using an autocorrelation procedure.
  • Experimental validation involving five Parkinson's disease patients and five healthy adults.

    • Main Results:

    • The wearable system accurately estimated key gait cycle parameters in real-time.
    • Significant differences in gait parameters were identified between Parkinson's disease patients and healthy controls.
    • The autocorrelation procedure proved effective for distinguishing between different mobility groups.

    Conclusions:

    • The developed wearable accelerometry system is effective for real-time gait cycle parameter recognition and differentiation of mobility impairments.
    • This technology holds potential for applications in ambulatory rehabilitation, gait assessment, and personal telecare for individuals with gait disorders.
    • Future research can extend this system for detecting specific disabling gaits like festination or freezing of gait in PD patients.