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Empowering Lower Limb Disorder Identification through PoseNet and Artificial Intelligence.

Hafeez Ur Rehman Siddiqui1, Adil Ali Saleem1, Muhammad Amjad Raza1

  • 1Institute of Computer Science, Khwaja Fareed University of Engineering and Information Technology, Abu Dhabi Road, Rahim Yar Khan 64200, Punjab, Pakistan.

Diagnostics (Basel, Switzerland)
|September 28, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a new method using PoseNet and machine learning to classify lower limb disorders like knee, hip, and ankle issues from gait analysis. Artificial Neural Networks achieved 98.84% accuracy, offering a promising non-invasive diagnostic tool.

Keywords:
Artificial Neural NetworksPoseNetgait analysislower limb disordermachine learning

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Area of Science:

  • Biomechanics
  • Medical Imaging
  • Machine Learning

Background:

  • Lower limb disorders affect mobility and quality of life.
  • Accurate diagnosis is crucial for effective treatment planning.
  • Current diagnostic methods can be invasive or lack detailed kinematic information.

Purpose of the Study:

  • To develop and evaluate a novel, non-invasive method for classifying lower limb disorders.
  • To utilize gait analysis and PoseNet features for identifying knee, hip, and ankle conditions.
  • To compare the performance of various machine learning algorithms for this classification task.

Main Methods:

  • Gait analysis using video data and the PoseNet algorithm to extract key joint movements.
  • Standardization of extracted features for input into machine learning models.
  • Training and testing of Random Forest, Extra Tree Classifier, Multilayer Perceptron, Artificial Neural Networks (ANN), and Convolutional Neural Networks (CNN) using K-fold cross-validation on a dataset of 174 patients.

Main Results:

  • The study achieved high accuracy and precision in classifying lower limb disorders.
  • Artificial Neural Networks (ANN) demonstrated the highest classification accuracy at 98.84%.
  • The proposed method proved effective in differentiating various lower limb conditions.

Conclusions:

  • The developed methodology offers a non-invasive and efficient approach for diagnosing lower limb disorders.
  • PoseNet-based gait analysis combined with machine learning shows significant potential for improving diagnostic accuracy.
  • This approach can aid in better treatment planning for patients with knee, hip, and ankle conditions.