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Computers in biology and medicine v.95, 2018년, pp.277 - 287   SCI SCIE SCOPUS
본 등재정보는 저널의 등재정보를 참고하여 보여주는 베타서비스로 정확한 논문의 등재여부는 등재기관에 확인하시기 바랍니다.

Codebook-based electrooculography data analysis towards cognitive activity recognition

Lagodzinski, P. (Department of Knowledge Engineering, University of Economics in Katowice, Bogucicka 3 Str., 40-226 Katowice, Poland ) ; Shirahama, K. (Pattern Recognition Group, University of Siegen, Hoelderlinstr. 3, 57076 Siegen, Germany ) ; Grzegorzek, M. (Department of Knowledge Engineering, University of Economics in Katowice, Bogucicka 3 Str., 40-226 Katowice, Poland ) ;
  • 초록  

    Abstract With the advancement in mobile/wearable technology, people started to use a variety of sensing devices to track their daily activities as well as health and fitness conditions in order to improve the quality of life. This work addresses an idea of eye movement analysis, which due to the strong correlation with cognitive tasks can be successfully utilized in activity recognition. Eye movements are recorded using an electrooculographic (EOG) system built into the frames of glasses, which can be worn more unobtrusively and comfortably than other devices. Since the obtained information is low-level sensor data expressed as a sequence representing values in constant intervals (100 Hz), the cognitive activity recognition problem is formulated as sequence classification. However, it is unclear what kind of features are useful for accurate cognitive activity recognition. Thus, a machine learning algorithm like a codebook approach is applied, which instead of focusing on feature engineering is using a distribution of characteristic subsequences (codewords) to describe sequences of recorded EOG data, where the codewords are obtained by clustering a large number of subsequences. Further, statistical analysis of the codeword distribution results in discovering features which are characteristic to a certain activity class. Experimental results demonstrate good accuracy of the codebook-based cognitive activity recognition reflecting the effective usage of the codewords. Highlights Cognitive activity recognition based on EOG obtained from smart eyewear. Codebook approach to extract useful features for accurate activity recognition. Statistical analysis to discover subsequences characteristic to activities. Very accurate recognition on activities like reading, watching video and drinking.


  • 주제어

    Ambient assisted living .   Cognitive activity recognition .   Electrooculography (EOG) .   Sequence classification .   Codebook approach.  

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