title: Improving Eye Motion Sequence Recognition Using Electrooculography Based on Context-Dependent HMM.
abstract: 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.
authors: Department of Information Processing, Tokyo Institute of Technology, Yokohama, Japan.
authors: Fuming
authors: 0000-0002-9332-3735
authors: F
authors: Fang
authors: Department of Information Processing, Tokyo Institute of Technology, Yokohama, Japan.
authors: Takahiro
authors: T
authors: Shinozaki
authors: Division of Information Sciences, Chiba University, Chiba, Japan.
authors: Yasuo
authors: Y
authors: Horiuchi
authors: Division of Information Sciences, Chiba University, Chiba, Japan.
authors: Shingo
authors: S
authors: Kuroiwa
authors: Department of Computer Science, Tokyo Institute of Technology, Tokyo, Japan.
authors: Sadaoki
authors: S
authors: Furui
authors: Brain Functions Laboratory Inc., Yokohama, Japan.
authors: Toshimitsu
authors: T
authors: Musha
mesh_terms: D000465:Algorithms; D066264:Datasets as Topic; D004585:Electrooculography; D005133:Eye Movements / Q000502:physiology*; D006801:Humans; D008328:Man-Machine Systems; D008390:Markov Chains; D009038:Motion; D012815:Signal Processing, Computer-Assisted