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Depth-aware pose estimation using deep learning for exoskeleton gait analysis.

Yachun Wang1, Zhongcai Pei1, Chen Wang1

  • 1School of Automation Science and Electrical Engineering, Beihang University, Beijing, 100191, China.

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|December 19, 2023
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Summary
This summary is machine-generated.

This study introduces GaitPoseNet, a computer vision method for real-time gait analysis in exoskeleton-assisted walking. It enhances rehabilitation safety by accurately detecting gait abnormalities and preventing patient injuries.

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Area of Science:

  • Rehabilitation Medicine
  • Computer Vision
  • Biomechanical Engineering

Background:

  • Real-time gait analysis is crucial for preventing injuries during lower-limb exoskeleton rehabilitation.
  • Current methods may struggle with accuracy and real-time monitoring during human-machine integrated walking.

Purpose of the Study:

  • To develop a non-contact, accurate, and real-time gait detection method for exoskeleton-assisted rehabilitation.
  • To improve patient safety and training effectiveness by monitoring gait parameters.

Main Methods:

  • A novel neural network, GaitPoseNet, was designed for posture recognition using RGB images and depth estimation.
  • A joint guidance strategy (JGS) was incorporated to handle partial joint occlusion.
  • A post-processing algorithm combined joint coordinates and leg length for motion description.

Main Results:

  • GaitPoseNet achieved high accuracy, with 95.77% PCKs@0.1 and 93.14% PCKs@0.08 on the WPE Dataset.
  • The method demonstrated a fast runtime of 3.55 ms, indicating real-time applicability.
  • The non-contact approach with JGS improved measurement accuracy and universality.

Conclusions:

  • The proposed GaitPoseNet offers an advanced, non-contact solution for real-time gait monitoring in exoskeleton rehabilitation.
  • The combination of depth estimation and JGS effectively addresses challenges like joint occlusion, enhancing accuracy.
  • This method significantly contributes to safer and more effective rehabilitation training by providing precise gait analysis.