Improving Eye Motion Sequence Recognition Using Electrooculography Based on Context-Dependent HMM
Joint Authors
Fang, Fuming
Shinozaki, Takahiro
Horiuchi, Yasuo
Kuroiwa, Shingo
Furui, Sadaoki
Musha, Toshimitsu
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2016, Issue 2016 (31 Dec. 2015), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-09-27
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
Eye motion-based human-machine interfaces are used to provide a means of communication for those who can move nothing but their eyes because of injury or disease.
To detect eye motions, electrooculography (EOG) is used.
For efficient communication, the input speed is critical.
However, it is difficult for conventional EOG recognition methods to accurately recognize fast, sequentially input eye motions because adjacent eye motions influence each other.
In this paper, we propose a context-dependent hidden Markov model- (HMM-) based EOG modeling approach that uses separate models for identical eye motions with different contexts.
Because the influence of adjacent eye motions is explicitly modeled, higher recognition accuracy is achieved.
Additionally, we propose a method of user adaptation based on a user-independent EOG model to investigate the trade-off between recognition accuracy and the amount of user-dependent data required for HMM training.
Experimental results show that when the proposed context-dependent HMMs are used, the character error rate (CER) is significantly reduced compared with the conventional baseline under user-dependent conditions, from 36.0 to 1.3%.
Although the CER increases again to 17.3% when the context-dependent but user-independent HMMs are used, it can be reduced to 7.3% by applying the proposed user adaptation method.
American Psychological Association (APA)
Fang, Fuming& Shinozaki, Takahiro& Horiuchi, Yasuo& Kuroiwa, Shingo& Furui, Sadaoki& Musha, Toshimitsu. 2016. Improving Eye Motion Sequence Recognition Using Electrooculography Based on Context-Dependent HMM. Computational Intelligence and Neuroscience،Vol. 2016, no. 2016, pp.1-9.
https://search.emarefa.net/detail/BIM-1099741
Modern Language Association (MLA)
Fang, Fuming…[et al.]. Improving Eye Motion Sequence Recognition Using Electrooculography Based on Context-Dependent HMM. Computational Intelligence and Neuroscience Vol. 2016, no. 2016 (2015), pp.1-9.
https://search.emarefa.net/detail/BIM-1099741
American Medical Association (AMA)
Fang, Fuming& Shinozaki, Takahiro& Horiuchi, Yasuo& Kuroiwa, Shingo& Furui, Sadaoki& Musha, Toshimitsu. Improving Eye Motion Sequence Recognition Using Electrooculography Based on Context-Dependent HMM. Computational Intelligence and Neuroscience. 2016. Vol. 2016, no. 2016, pp.1-9.
https://search.emarefa.net/detail/BIM-1099741
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1099741