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Wearable Sensor-Based Real-Time Gait Detection: A Systematic Review.

Hari Prasanth1,2, Miroslav Caban3,4, Urs Keller4

  • 1ONWARD, Building 32, Hightech Campus, 5656 AE Eindhoven, The Netherlands.

Sensors (Basel, Switzerland)
|April 30, 2021
PubMed
Summary
This summary is machine-generated.

Wearable sensors like IMUs enable real-time gait analysis for rehabilitation. Combining IMUs with rule-based methods offers an optimal solution for clinical gait detection, especially for pathological gaits.

Keywords:
assistive devicegait analysisgait rehabilitationinertial measurement unitinsole pressure sensorspathological gaitreal-time gait detectionwearable sensor

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Area of Science:

  • Biomedical Engineering
  • Rehabilitation Technology
  • Wearable Sensor Technology

Background:

  • Traditional gait analysis relies on expensive lab equipment.
  • Wearable sensors offer affordable, real-time gait analysis for clinical use and daily living.
  • Real-time gait analysis is crucial for developing rehabilitation techniques and assistive devices.

Purpose of the Study:

  • To systematically review wearable sensors and techniques for real-time gait analysis.
  • To assess their application in pathological gait analysis.
  • To identify optimal solutions for clinical gait detection.

Main Methods:

  • Systematic review of 1262 articles from four major scientific databases.
  • Full-text analysis of 113 selected articles.
  • Identification of commonly analyzed gait events, sensor types, placements, and detection methods.

Main Results:

  • Heel strike and toe off are the most targeted gait events.
  • Inertial Measurement Units (IMUs) are the most prevalent sensors, often placed on the shank or foot.
  • Rule-based techniques are the most common detection methods, with insole pressure sensors used for validation.
  • Less than a third of studies validated methods on pathological gait data.

Conclusions:

  • A combination of IMU sensors and rule-based methods is recommended as an optimal solution for clinical gait detection.
  • Further validation on pathological gait data is needed.
  • Real-time gait analysis with wearable sensors shows significant potential for clinical applications.