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A Movement Classification of Polymyalgia Rheumatica Patients Using Myoelectric Sensors.

Anthony Bawa1, Konstantinos Banitsas1, Maysam Abbod1

  • 1Department of Electronic and Electrical Engineering, Brunel University London, Uxbridge UB8 3PH, UK.

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Summary
This summary is machine-generated.

Machine learning accurately detects gait disorders in polymyalgia rheumatica patients. Support vector machine models show high accuracy, aiding rehabilitation and diagnosis.

Keywords:
classifiersgait disorderpatternpolymyalgia rheumatica

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Area of Science:

  • Biomedical Engineering
  • Clinical Biomechanics
  • Machine Learning in Healthcare

Background:

  • Gait disorders are prevalent in neurological and musculoskeletal conditions.
  • Accurate gait analysis is crucial for effective rehabilitation planning.
  • Polymyalgia rheumatica (PMR) can significantly impact mobility and gait.

Purpose of the Study:

  • To analyze clinical gait patterns in patients with polymyalgia rheumatica.
  • To differentiate gait impairments in PMR patients from healthy controls using machine learning.
  • To evaluate the efficacy of various machine learning models for gait disorder detection in PMR.

Main Methods:

  • Clinical gait assessment involving 18 PMR patients and 7 controls.
  • Collection of electromyography data from four hip muscles (rectus femoris, vastus lateralis, biceps femoris, semitendinosus).
  • Application of Support Vector Machine (SVM), Rotation Forest (RF), K-Nearest Neighbors (KNN), and Decision Tree (DT) classification models.

Main Results:

  • Support Vector Machine (SVM) achieved the highest accuracy (85%) and sensitivity (92%).
  • Other models showed varying performance: RF (80% accuracy, 86% sensitivity), KNN (75% accuracy, 84% sensitivity), DT (70% accuracy, 90% sensitivity).
  • Machine learning models effectively discriminated between PMR patients and control subjects' gait patterns.

Conclusions:

  • Machine learning, particularly SVM, demonstrates high potential for identifying gait disorders in polymyalgia rheumatica.
  • These findings can inform the design of targeted therapeutic exercises for PMR patients.
  • The developed models may serve as a decision support system for diagnosing gait impairments in PMR.