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Efficient EOG-based movement classification in IoMT using machine learning algorithms for people with motor

Saly Abd-Elateif El-Gindy1, Walid El-Shafai2, Naglaa F Soliman3

  • 1High Institute for Engineering & Technology-Al Obour, Al Obour City, Egypt.

Disability and Rehabilitation. Assistive Technology
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Summary
This summary is machine-generated.

This study introduces an Internet of Medical Things (IoMT) platform using electrooculography (EOG) for smart home control by patients with motor disabilities. The system achieved high accuracy, demonstrating effective EOG-based assistive technology.

Keywords:
Internet of Medical Things (IoMT)Machine Learning (ML) algorithmsStockwell transform (S-transform)electrooculogram (EOG)wavelet transform

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Area of Science:

  • Biomedical Engineering
  • Assistive Technology
  • Signal Processing

Background:

  • Motor disabilities significantly impact independence and quality of life.
  • Smart home technologies offer potential for enhanced autonomy.
  • Effective human-computer interfaces are crucial for assistive systems.

Purpose of the Study:

  • To develop and evaluate an Internet of Medical Things (IoMT) platform for real-time smart home control using electrooculography (EOG).
  • To enable patients with motor disabilities to interact with and manage their home environment.
  • To investigate the efficacy of signal processing and machine learning techniques for EOG-based control.

Main Methods:

  • Utilized electrooculography (EOG) signals for user interaction.
  • Applied Stockwell transform (S-transform) and wavelet transform for EOG signal analysis.
  • Employed Daubechies (db4) and Symlets (Sym4) wavelet families for feature extraction.
  • Classified eye movements using Support Vector Machines (SVM), Kernel Neural Networks (KNN), Ensemble Tree (ET), and Convolutional Neural Networks (CNN).

Main Results:

  • Achieved a high average accuracy of 97.7% with the SVM classifier and db4 wavelet.
  • Demonstrated superior performance compared to previous methods.
  • The db4 wavelet provided better results than the Sym4 wavelet for EOG signal classification.

Conclusions:

  • The proposed IoMT platform effectively utilizes EOG signals for smart home control in patients with motor disabilities.
  • The combination of S-transform, wavelet transform (specifically db4), and SVM offers a robust and accurate solution.
  • This technology has significant potential to improve the independence and quality of life for individuals with motor impairments.