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HMM based automated wheelchair navigation using EOG traces in EEG.

Fayeem Aziz1, Hamzah Arof, Norrima Mokhtar

  • 1Department of Electrical Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia.

Journal of Neural Engineering
|September 5, 2014
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Summary

This study introduces a novel wheelchair navigation system using electrooculography (EOG) signals and a hidden Markov model (HMM). The system offers semi-autonomous control for individuals with restricted mobility, achieving high accuracy and fast response times.

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Area of Science:

  • Biomedical Engineering
  • Rehabilitation Technology
  • Human-Computer Interaction

Background:

  • Individuals with restricted mobility often face challenges with traditional wheelchair control.
  • Existing assistive technologies may have limitations in terms of intuitiveness and responsiveness.
  • Electrooculography (EOG) offers a potential non-invasive method for control signals.

Purpose of the Study:

  • To develop and evaluate a semi-autonomous wheelchair navigation system controlled by electrooculography (EOG) signals.
  • To integrate a hidden Markov model (HMM) for robust command generation and state determination.
  • To provide an intuitive and efficient control solution for individuals with limited mobility.

Main Methods:

  • Utilized electrooculography (EOG) signals from scalp EEG to detect eye movements (open/closed, gaze direction).
  • Extracted features from EOG signals and used them as inputs for support vector machine (SVM) classifiers.
  • Employed a hidden Markov model (HMM) to interpret SVM outputs as observations for wheelchair navigation commands.
  • Integrated obstacle/collision avoidance sensors and a proximity sensor for enhanced safety.

Main Results:

  • Achieved an average classification rate of 98% for EOG signal interpretation with online data.
  • Demonstrated a fast average execution time of less than 1 second for system commands.
  • Successfully completed navigation tasks without collisions in experiments with all participants.

Conclusions:

  • The developed HMM-based wheelchair navigation system effectively translates EOG signals into reliable control commands.
  • The system demonstrates high accuracy, rapid execution, and successful navigation capabilities.
  • This technology holds significant promise for enhancing independence and quality of life for individuals with mobility impairments.