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Gait Partitioning Methods: A Systematic Review.

Juri Taborri1, Eduardo Palermo2, Stefano Rossi3

  • 1Department of Mechanical and Aerospace Engineering, Sapienza University of Roma, Via Eudossiana 18, Roma I-00184, Italy. juri.taborri@uniroma1.it.

Sensors (Basel, Switzerland)
|January 12, 2016
PubMed
Summary

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This summary is machine-generated.

This study reviews sensor technologies for gait phase detection, comparing wearable and non-wearable options. It analyzes methods, sensor placement, and data granularity for accurate gait analysis.

Area of Science:

  • Biomechanics and Human Movement Science
  • Sensor Technology and Signal Processing
  • Rehabilitation Engineering

Background:

  • Gait phase partitioning is crucial for gait technology applications, presenting ongoing research challenges.
  • Diverse sensors, both wearable and non-wearable, are employed for gait phase detection.
  • Traditional methods like footswitches and advanced inertial measurement units (IMUs) are popular wearable options.

Purpose of the Study:

  • To identify, select, and categorize existing methodologies for gait phase detection.
  • To analyze the advantages and disadvantages of various gait phase detection sensor systems.
  • To comparatively examine gait phase granularity, computational approaches, and optimal sensor placement.

Main Methods:

  • Systematic review and categorization of wearable (IMUs, foot pressure insoles, EMG, etc.) and non-wearable (opto-electronic systems, force platforms) sensors.
Keywords:
electromyography (EMG)footswitchesforce platformgait patterngait phase partitioninginertial measurements units (IMU)opto-electronic systemwearable sensors

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  • Analysis of sensor performance based on accuracy, limitations, and application suitability.
  • Comparative examination of gait phase detection algorithms, sensor placement strategies, and achievable data resolution.

    • Main Results:

    • Wearable sensors like IMUs offer practical solutions, while footswitches and pressure insoles serve as benchmarks.
    • Non-wearable systems, particularly opto-electronic setups with force platforms, provide the highest accuracy in controlled indoor settings.
    • Different sensor types yield varying levels of gait phase granularity and require specific computational methods and placement.

    Conclusions:

    • A comprehensive understanding of sensor capabilities and limitations is essential for effective gait phase partitioning.
    • The choice of sensor technology depends on the specific application requirements, desired accuracy, and environmental constraints.
    • Further research into optimal sensor placement and advanced computational techniques can enhance the precision of gait analysis.