Improving eye motion sequence recognition using electrooculography based on context-dependent HMM

Autor: Takahiro Shinozaki, Yasuo Horiuchi, Shingo Kuroiwa, Sadaoki Furui, Toshimitsu Musha, Fuming Fang
Jazyk: angličtina
Rok vydání: 2016
Předmět:
Article Subject
Eye Movements
General Computer Science
Computer science
General Mathematics
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Datasets as Topic
Word error rate
Adaptation (eye)
Context (language use)
02 engineering and technology
lcsh:Computer applications to medicine. Medical informatics
Motion (physics)
lcsh:RC321-571
Motion
0202 electrical engineering
electronic engineering
information engineering
medicine
Humans
Computer vision
Hidden Markov model
lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry
Man-Machine Systems
Markov chain
medicine.diagnostic_test
business.industry
General Neuroscience
Eye movement
Signal Processing
Computer-Assisted
020207 software engineering
General Medicine
Electrooculography
Markov Chains
ComputingMethodologies_PATTERNRECOGNITION
lcsh:R858-859.7
020201 artificial intelligence & image processing
Artificial intelligence
business
Algorithms
Research Article
Zdroj: Computational Intelligence and Neuroscience
Computational Intelligence and Neuroscience, Vol 2016 (2016)
Popis: 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.
Databáze: OpenAIRE
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