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Hybrid Deep Neural Network Framework Combining Skeleton and Gait Features for Pathological Gait Recognition.

Kooksung Jun1,2, Keunhan Lee3, Sanghyub Lee2

  • 1Robocare, Seongnam 13449, Republic of Korea.

Bioengineering (Basel, Switzerland)
|October 28, 2023
PubMed
Summary

Combining skeleton data, joint angles, and gait parameters significantly improves pathological gait recognition. This hybrid deep learning model enhances diagnostic support for physicians by outperforming single-input methods.

Keywords:
feature fusionhybrid deep neural networkpathological gait recognitionskeleton-based gait analysis

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Area of Science:

  • Biomechanics
  • Computer Science
  • Medical Imaging

Background:

  • Pathological gait recognition aids physician diagnosis.
  • Existing methods often use raw skeleton data or extracted gait features.
  • Combining diverse data types may enhance recognition accuracy.

Purpose of the Study:

  • To develop a deep neural network (DNN) model for improved pathological gait recognition.
  • To investigate the efficacy of combining skeleton sequences, joint angles, and gait parameters.
  • To create a hybrid DNN framework for effective multi-modal data fusion.

Main Methods:

  • A hybrid DNN framework integrating Graph Convolutional Networks (GCN), Recurrent Neural Networks (RNN), and Artificial Neural Networks (ANN).
  • GCN for skeleton sequences, RNN for joint angle sequences, and ANN for gait parameters.
  • Feature fusion from all three input types before final classification.

Main Results:

  • The proposed hybrid model demonstrated improved pathological gait recognition performance on two datasets (simulated and vestibular disorder).
  • Multi-modal input significantly outperformed single-input models.
  • The model achieved state-of-the-art results for skeleton-based action recognition.

Conclusions:

  • Integrating skeleton data, joint angles, and gait parameters enhances pathological gait recognition.
  • The hybrid DNN framework offers a robust approach for multi-modal gait analysis.
  • This method shows promise for improving diagnostic decision-making in clinical settings.