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Pubmed for Handhelds

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  • Title: Real-Time "Eye-Writing" Recognition Using Electrooculogram.
    Author: Kwang-Ryeol Lee, Won-Du Chang, Sungkean Kim, Chang-Hwan Im.
    Journal: IEEE Trans Neural Syst Rehabil Eng; 2017 Jan; 25(1):37-48. PubMed ID: 28113859.
    Abstract:
    Eye movements can be used as alternative inputs for human-computer interface (HCI) systems such as virtual or augmented reality systems as well as new communication ways for patients with locked-in syndrome. In this study, we developed a real-time electrooculogram (EOG)-based eye-writing recognition system, with which users can write predefined symbolic patterns with their volitional eye movements. For the "eye-writing" recognition, the proposed system first reconstructs the eye-written traces from EOG waveforms in real-time; then, the system recognizes the intended symbolic inputs with a reliable recognition rate by matching the input traces with the trained eye-written traces of diverse input patterns. Experiments with 20 participants showed an average recognition rate of 87.38% (F1 score) for 29 different symbolic patterns (26 lower case alphabet characters and three functional input patterns representing Space, Backspace, and Enter keys), demonstrating the promise of our EOG-based eye-writing recognition system in practical scenarios.
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