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A Two-stream Convolutional Network for Musculoskeletal and Neurological Disorders Prediction.

Manli Zhu1, Qianhui Men2, Edmond S L Ho3

  • 1Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne, UK.

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Summary

This study introduces a novel deep learning approach for analyzing walking problems in older adults. The method enhances diagnostic accuracy by integrating joint and inter-joint movement data, achieving 95.56% prediction accuracy.

Keywords:
Convolutional neural networkDeep learningFeature fusionMusculoskeletal disordersNeurological disorders

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Area of Science:

  • Biomechanics
  • Medical Imaging and Data Analysis
  • Artificial Intelligence in Healthcare

Background:

  • Musculoskeletal and neurological disorders frequently cause mobility issues in the elderly, impacting their quality of life.
  • Manual analysis of walking data is time-consuming, requires expertise, and can be subjective.
  • Automated analysis using deep learning shows potential for early diagnosis by identifying complex patterns.

Purpose of the Study:

  • To develop an automated system for analyzing walking problems in older adults.
  • To improve diagnostic accuracy by incorporating both individual joint and inter-joint movement features.
  • To overcome limitations of existing deep learning methods that focus solely on individual joint data.

Main Methods:

  • A novel two-stream deep learning framework was proposed, processing joint position and relative joint displacement time series separately.
  • A mid-layer fusion module was developed to integrate features from both streams for enhanced pattern recognition.
  • The system was validated using a 3D skeleton motion dataset from 45 patients with mobility-affecting disorders.

Main Results:

  • The proposed two-stream framework achieved a prediction accuracy of 95.56% in diagnosing walking problems.
  • This accuracy significantly outperforms existing state-of-the-art methods.
  • The integration of inter-joint features proved crucial for improved performance.

Conclusions:

  • The developed deep learning system effectively analyzes walking motion data for diagnosing musculoskeletal and neurological disorders.
  • Explicitly incorporating inter-joint features alongside individual joint data enhances diagnostic prediction.
  • This approach offers a promising tool for objective and early detection of mobility impairments in older adults.