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Related Experiment Video

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Lateral Walking Gait Recognition and Hip Angle Prediction Using a Dual-Task Learning Framework.

Mingxiang Luo1, Meng Yin1, Jinke Li1

  • 1Guangdong Provincial Key Lab of Robotics and Intelligent System, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.

Cyborg and Bionic Systems (Washington, D.C.)
|May 2, 2025
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Summary

This study introduces the "Twin Brother" model for accurate lateral walking gait recognition and hip angle prediction using electromyography (EMG) signals. The model significantly improves exoskeleton control by achieving high accuracy in classifying gait stages and estimating joint angles.

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Area of Science:

  • Biomechanics and Biomedical Engineering
  • Machine Learning in Healthcare
  • Rehabilitation Robotics

Background:

  • Lateral walking enhances hip abductor strength, crucial for mobility.
  • Accurate gait recognition and hip joint angle prediction are vital for controlling assistive devices like exoskeletons.
  • Electromyography (EMG) signals offer a promising avenue for real-time biomechanical analysis.

Purpose of the Study:

  • To develop and validate a novel dual-task learning framework, the "Twin Brother" model.
  • To accurately classify lateral walking gait stages and estimate continuous hip joint angles from EMG signals.
  • To enhance the control capabilities of exoskeletons for improved rehabilitation and assistance.

Main Methods:

  • A dual-task learning framework fusing Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Neural Networks (NNs), and an attention mechanism was employed.
  • Electromyography (EMG) signals from six muscles of ten subjects during lateral walking were collected and analyzed.
  • The model was evaluated against traditional machine learning methods, including CNN-LSTM, CNN, LSTM, Support Vector Machine (SVM), NN, and K-Nearest Neighbors (KNN).

Main Results:

  • The "Twin Brother" model achieved a high gait recognition accuracy of 98.81% ± 0.14%.
  • Excellent hip angle prediction accuracy was demonstrated with Root Mean Square Errors (RMSE) of 0.9183° ± 0.024° (left) and 1.0511° ± 0.027° (right), and R² values of 0.9853 ± 0.006 and 0.9808 ± 0.008, respectively.
  • The model outperformed all comparative methods in both gait recognition and hip angle estimation tasks.

Conclusions:

  • The proposed "Twin Brother" model offers a robust and accurate solution for real-time lateral walking gait recognition and hip joint angle prediction.
  • This advancement holds significant potential for improving the precision and effectiveness of exoskeleton control systems.
  • The findings pave the way for more sophisticated human-robot interaction in rehabilitation and assistive technologies.