| dc.description.abstract | In patients with amyotrophic lateral sclerosis (ALS), due to the degeneration
of motor neurons, the muscles will gradually atrophy, lose exercise ability, and
cannot use oral and physical communication. Eye moving function that
degenerates later may become the only way for the later-stage patients to rely on
to communicate with others.
This study uses Arabic numerals (0~9), uppercase English letters (A~Z) and
4 special symbols (space, input, dot, question mark), a total of 40 symbols, to
establish the eye-writing system. This system uses electrooculography (EOG) to
record eye movements. It converts the EOG signals into images for symbol
recognition with a convolutional neural network (CNN). It uses the number of
blinks as special commands to control the entering and exiting of the writing and
recognition subsystem, the start and end of writing, character selection and
deletion. The system functions to emulate general handwriting and typing
situations.
The data required for model training include the original data collected by
eye-writing all symbols for multiple times and the augmented data. The CNN is
trained by k-fold cross-validation. When the system is practically used by the user,
any symbol trace that leads to successful character recognition or selection can be
used to retrain the neural network model to make the model gradually become
more robust. The more the system is used and retrained, the more it will fit the
user′s writing habits, and the accuracy of eye-writing recognition will be gradually
improved. | en_US |