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Electrooculography-based continuous eye-writing recognition system for efficient assistive communication systems.

Fuming Fang1, Takahiro Shinozaki1

  • 1Department of Information and Communication Engineering, School of Engineering, Tokyo Institute of Technology, Yokohama, Kanagawa, Japan.

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This study introduces a continuous eye-writing system for locked-in syndrome patients, enabling faster communication. The system uses electrooculography (EOG) and hidden Markov models (HMM) for rapid, accurate character recognition.

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Area of Science:

  • Biomedical Engineering
  • Human-Computer Interaction
  • Neuroscience

Background:

  • Eye-movement based human-computer interfaces can aid communication for patients with locked-in syndrome.
  • Existing eye-writing systems are slow due to required pauses between characters for recognition.
  • Continuous input methods are needed to improve communication rates for these patients.

Purpose of the Study:

  • To develop and evaluate a continuous eye-writing recognition system for faster communication.
  • To improve input rates for patients with locked-in syndrome using eye movements.
  • To investigate the effectiveness of electrooculography (EOG) and hidden Markov models (HMM) for continuous eye-writing recognition.

Main Methods:

  • Eye movements detected using electrooculography (EOG).
  • Hidden Markov model (HMM) applied for continuous eye-written character recognition.
  • Deep neural network (DNN)-based HMM investigated for EOG signal adaptation.

Main Results:

  • Achieved an average input speed of 27.9 characters per minute using Japanese Katakana.
  • Attained a Katakana character-recognition error rate of 5.0%.
  • Demonstrated effectiveness with 13.8 minutes of adaptation data.

Conclusions:

  • The proposed continuous eye-writing system significantly increases input speed for patients with locked-in syndrome.
  • The EOG and HMM-based approach offers a viable method for rapid and accurate communication.
  • Further development could enhance usability and accessibility for assistive communication technologies.