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Joint angle estimation with wavelet neural networks.

Saaveethya Sivakumar1,2, Alpha Agape Gopalai3, King Hann Lim4

  • 1School of Engineering, Monash University Malaysia, Bandar Sunway, Malaysia. saaveethya@ieee.org.

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Summary
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This study introduces a wavelet neural network (WNN) method for gait analysis, reducing the need for wearable kinematic sensors. The approach uses foot kinetic sensor data to estimate joint angles, simplifying outdoor gait monitoring.

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Area of Science:

  • Biomechanics
  • Biomedical Engineering
  • Machine Learning

Background:

  • Wearable kinematic sensors present challenges for real-time outdoor gait analysis due to complex placement and sensor offset errors.
  • Existing methods often require multiple body-mounted sensors, limiting practical applications.

Purpose of the Study:

  • To develop a novel method for gait analysis that minimizes reliance on wearable kinematic sensors.
  • To enable simplified and potentially more accurate real-time outdoor gait monitoring.

Main Methods:

  • A wavelet neural network (WNN) model was employed to estimate lower limb joint angles (ankle, knee, hip).
  • Vertical Ground Reaction Forces (vGRFs) from foot kinetic sensors were used as input data.
  • Salient vGRF features were extracted from key gait event intervals.

Main Results:

  • The WNN method successfully estimated joint angles using vGRF inputs, demonstrating the feasibility of sensor reduction.
  • The approach showed potential for simplified sensor configurations without compromising overall gait analysis performance.

Conclusions:

  • The proposed WNN-based method offers a viable alternative to traditional wearable kinematic sensors in gait analysis.
  • This technique facilitates the integration of smart insoles for enhanced real-time outdoor gait studies and simplified sensor setups.