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A Lightweight Pose Sensing Scheme for Contactless Abnormal Gait Behavior Measurement.

Yuliang Zhao1,2, Jian Li1,2, Xiaoai Wang1,2

  • 1Sensor and Big Data Laboratory, Northeastern University, Qinhuangdao 066000, China.

Sensors (Basel, Switzerland)
|June 10, 2022
PubMed
Summary

This study introduces a contactless method for recognizing abnormal gait using a monocular camera and OpenPose (OP) model. The approach achieves high precision (92.13%) for gait analysis, overcoming wearable sensor limitations.

Keywords:
OpenPoseXGBoostabnormal gait behaviormachine learningrandom forest

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Area of Science:

  • Biomechanics
  • Medical Imaging
  • Machine Learning

Background:

  • Abnormal gait recognition is crucial for motion assessment and disease diagnosis.
  • Current methods using wearable sensors face challenges like data drift and patient discomfort.
  • Developing contactless, accurate gait analysis is essential for broader clinical application.

Purpose of the Study:

  • To propose a contactless method for abnormal gait behavior recognition using a monocular camera.
  • To develop a lightweight OpenPose (OP) model for real-time joint point extraction and 3D gait reconstruction.
  • To evaluate the effectiveness of machine learning algorithms for classifying abnormal gait features.

Main Methods:

  • A monocular camera captures human pose data, processed by a lightweight OpenPose (OP) model with Depthwise Separable Convolution.
  • Extracted 2D joint coordinates are reconstructed into 3D data to extract 11 types of abnormal gait features.
  • The XGBoost algorithm is used for feature screening, followed by Random Forest (RF) classification.

Main Results:

  • The proposed method successfully recognizes abnormal gait behavior in real time.
  • The Random Forest (RF) algorithm combined with 3D gait features achieved the highest precision of 92.13%.
  • The system demonstrates robustness against data drift and eliminates the need for wearable sensors.

Conclusions:

  • The contactless gait recognition method offers a viable alternative to traditional wearable sensors.
  • This approach reduces hardware requirements and overcomes challenges associated with sensor use, particularly in the elderly.
  • The real-time, contactless capabilities position this method for wide application in abnormal gait measurement and diagnostics.