전기학회논문지 (The Transactions of The Korean Institute of Electrical Engineers)

  • 제66권7호
  • /
  • Pages.1111-1116
  • /
  • 2017
  • /
  • 1975-8359(pISSN)
  • /
  • 2287-4364(eISSN)
대한전기학회 (The Korean Institute of Electrical Engineers)
권호 표지
DOI QR코드

DOI QR Code

이중 기계학습 구조를 이용한 안구이동추적 기술개발

Development of Eye-Tracking System Using Dual Machine Learning Structure

  • Gang, Gyeong Woo (Sch. of Information, Communications and Electronics Engineering, Catholic University of Korea) ;
  • Min, Chul Hong (Central Reserch Institute, Synopex Co., Ltd) ;
  • Kim, Tae Seon (Sch. of Information, Communications and Electronics Engineering, Catholic University of Korea)
  • 투고 : 2017.05.31
  • 심사 : 2017.06.29
  • 발행 : 2017.07.01
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

In this paper, we developed bio-signal based eye tracking system using electrooculogram (EOG) and electromyogram (EMG) which measured simultaneously from same electrodes. In this system, eye gazing position can be estimated using EOG signal and we can use EMG signal at the same time for additional command control interface. For EOG signal processing, PLA algorithms are applied to reduce processing complexity but still it can guarantee less than 0.2 seconds of reaction delay time. Also, we developed dual machine learning structure and it showed robust and enhanced tracking performances. Compare to conventional EOG based eye tracking system, developed system requires relatively light hardware system specification with only two skin contact electrodes on both sides of temples and it has advantages on application to mobile equipments or wearable devices. Developed system can provide a different UX for consumers and especially it would be helpful to disabled persons with application to orthotics for those of quadriplegia or communication tools for those of intellectual disabilities.

키워드

참고문헌

  1. L. Y. Deng, C. L. Hsu, T. C. Lin, J. S. Tuan, and S. M. Chang, "EOG-based Human Computer Interface system development", Expert Systems with Applications, vol 37, no 4, pp. 3337-3343, Apr. 2010. https://doi.org/10.1016/j.eswa.2009.10.017
  2. N. Wanluk, S. Visitsattapongese, A. Juhong, and C. Pintabirooj, "Smart wheelchair based on eye tracking", IEEE BMEiCON, pp. 1-4, 2016.
  3. R. Barea, L. Boquete and S. Ortega, E. Lopez , and J. M. Rodriguez, "EOG-based eye movements codification for human computer interaction", Expert Systems with Applications, vol 39, no. 3, pp. 2677-2683, Feb. 2012. https://doi.org/10.1016/j.eswa.2011.08.123
  4. D. Novak, G. Chanel, and P. Guillotel, "Toward Commercial Applications of Affective Computing", IEEE Tras. Affective Computing, vol. 8, no. 2, pp. 145-147, Apr.-Jun. 2017. https://doi.org/10.1109/TAFFC.2017.2676318
  5. A. Kolesnikov, "Efficient Online Algorithms for the Polygonal Approximation of Trajectory Data", IEEE Inter. Conf. Mobile Data Management, vol. 1, Jun. 2011.
  6. M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten, "The weka data mining software: An update.", ACM SIGKDD Explorations Newsletter, vol. 11, no. 1, Jun. 2009.
  7. C. C. Chang and C. J. Lin, "LIBSVM: A Library for Support Vector Machines", Technical report, Department of Computer Science, National Taiwan University, pp. 1-39, 2001.
  8. G. Gang and T. Kim, Bio-signal-based eye tracking system using dual machine learning structure and eye tracking method using same, Korea Patent, KR-A-101539923, Jul. 2015.