Journal of the Korea Convergence Society (한국융합학회논문지)

Korea Convergence Society (한국융합학회)
권호 표지
DOI QR코드

DOI QR Code

Gaze Tracking with Low-cost EOG Measuring Device

저가형 EOG 계측장치를 이용한 시선추적

  • Jang, Seung-Tae (Department of Biomedical Engineering, Tongmyung University) ;
  • Lee, Jung-Hwan (Department of Biomedical Engineering, Tongmyung University) ;
  • Jang, Jae-Young (Department of Biomedical Engineering, Tongmyung University) ;
  • Chang, Won-Du (School of Electronic and Biomedical Engineering, Tongmyung University)
  • 장승태 (동명대학교 의공학과) ;
  • 이정환 (동명대학교 의공학과) ;
  • 장재영 (동명대학교 의공학과) ;
  • 장원두 (동명대학교 전자.의용공학부)
  • Received : 2018.08.30
  • Accepted : 2018.11.20
  • Published : 2018.11.28
Download PDF
⟨ Previous Next ⟩

Abstract

This paper describes the experiments of gaze tracking utilizing a low-cost electrooculogram measuring device. The goal of the experiments is to verify whether the low-cost device can be used for a complicated human-computer interaction tool, such as the eye-writing. Two experiments are conducted for this goal: a simple gaze tracking of four directional eye-movements, and eye-writing-which is to draw letters or shapes in a virtual space. Eye-written alphabets were obtained by two PSL-iEOGs and an Arduino Uno; they were classified by dynamic positional warping after preprocessed by a wavelet function. The results show that the expected recognition accuracy of the four-directional recognition is close to 90% when noises are controlled, and the similar median accuracy (90.00%) was achieved for the eye-writing when the number of writing patterns are limited to five. In future works, additional algorithms for stabilizing the signal need to be developed.

아두이노와 저가형 생체신호 증폭기를 사용하여 시선추적실험을 실시하고 결과를 분석하였다. 본 연구에서는 간단한 4방향의 시선이동 인식실험과 함께, 시선을 사용하여 영어 알파벳 등을 직접 쓰는 eye-writing 인식실험을 함께 진행함으로, 새롭게 구성한 안구전도 측정장치의 실용성을 평가하고, 더 나아가 저가형 안구전도 장치가 eye-writing과 같은 복잡한 사람-컴퓨터간 상호작용도구로 활용될 수 있는지를 분석하였다. 실험을 위해서 PSL-iEOG와 아두이노를 사용하는 저가형 안구전도 측정장치가 구성되었으며, 패턴분류를 위해 dynamic positional warping과 웨이블릿 변환이 사용되었다. 실험 결과, 저가형 측정장치는 비교적 단순한 알고리즘만으로도 외부 잡음이 유입되지 않은 경우 90%에 가까운 정확도로 시선방향을 인식할 수 있었으며, eye-writing의 경우에도 5개 패턴에 대해서 90%의 중위 정확도를 달성할 수 있었다. 그러나 패턴의 숫자가 증가함에 따라 정확도가 매우 감소하여, 다양한 패턴의 직접적인 입력이라는 eye-writing의 장점을 부각하기 위해서는 저가형 장치에 특화된 알고리즘의 개발 등 추가적인 연구가 필요할 것으로 여겨진다.

Keywords

OHHGBW_2018_v9n11_53_f0001.png 이미지

Fig. 1. EOG measuring device

OHHGBW_2018_v9n11_53_f0002.png 이미지

Fig. 2. Location of electrodes.

OHHGBW_2018_v9n11_53_f0003.png 이미지

Fig. 3. Experimental Procedure

OHHGBW_2018_v9n11_53_f0004.png 이미지

Fig. 4. Overal Structure of Algorithm

OHHGBW_2018_v9n11_53_f0005.png 이미지

Fig. 5. Median accuracy across subjects as increasing the number of classes

Table 1. Branches of constraint slope for DPW

OHHGBW_2018_v9n11_53_t0001.png 이미지

Table 2. Confusion matrix for eye-movement recognitions

OHHGBW_2018_v9n11_53_t0002.png 이미지

Table 3. Recognition accuracy of directional eye movements

OHHGBW_2018_v9n11_53_t0003.png 이미지

Table 4. Recognition results of eye-written characters. S, B, E denote space, back space, and enter symbols respectively.

OHHGBW_2018_v9n11_53_t0004.png 이미지

Table 5. Recognition accuracy according to subjects

OHHGBW_2018_v9n11_53_t0005.png 이미지

Table 6. Recognition accuracy of eye-writing

OHHGBW_2018_v9n11_53_t0006.png 이미지

References

  1. W. D. Chang, H.-S. Cha & C.-H. Im. (2016). Removing the Interdependency between Horizontal and Vertical Eye-movement Components in Electrooculograms. Sensors, 16(2), 227. https://doi.org/10.3390/s16020227
  2. C. H. Morimoto & M. R. M. Mimica. (2005). Eye Gaze Tracking Techniques for Interactive Applications. Computer Vision and Image Understanding. 98, 4-24. https://doi.org/10.1016/j.cviu.2004.07.010
  3. A. Bulling, D. Roggen & G. Troster. (2009). Wearable EOG goggles: Seamless Sensing and Context-awareness in Everyday Environments, Journal of Ambient Intelligent Smart Environent. 1, 157-171.
  4. W. D. Chang, H. S. Cha, D. Y. Kim, S. H. Kim & C. H. Im. (2017). Development of an Electrooculogram-based Eye-computer Interface for Communication of Indiv-id-als with Amyotrophic Lateral Sclerosis, Journal of Neuroengineering and Rehabilitation. 14(1), Article ID: 89.
  5. F. Fang & T.Shinozaki. (2018). Electrooculography-based Continuous Eye-writing Recognition System for Efficient Assistive Communication Systems. PLoS One, 13(2), Article ID: e0192684.
  6. S. Benedetto, M. Pedrotti, L. Minin, T. Baccino, A. Re & R. Montanari. (2011). Driver Workload and Eye Blink Duration, Transportation Research Part F, 14, 199-208. https://doi.org/10.1016/j.trf.2010.12.001
  7. B. D. Yetton, M. Niknazar, K.A. Duggan, E.A. McDevitt, L.N. Whitehurst, N. Sattari & S.C. Mednick. (2015). Automatic Detection of Rapid Eye Movements (REMs): A Machine Learning Spproach, Journal of Neuroscience Methods, 259, 72-82.
  8. R. Barea, L. Boquete, M. Mazo, E. Lopez. (2002). Wheelchair Guidance Strategies using EOG, Journal of Intelligent and Robotic Systems: Theory and Applications, 34(3), 279-299. https://doi.org/10.1023/A:1016359503796
  9. J. Z. Tsai, C. K. Lee, C. M. Wu, J. J. Wu , K. P. Kao. (2008). A Feasibility Study of an Eye-writing System Based on Electro-oculography. Journal of Medical and Biological Engineering, 28, 39-46.
  10. K. R. Lee, W. D. Chang, S. Kim, C. H. Im. (2017). Real-Time ‘Eye-Writing' Recognition using Electrooculogram (EOG). IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25(1), 37-48. https://doi.org/10.1109/TNSRE.2016.2542524
  11. D. Borghetti, A. Bruni, M. Fabbrini, L. Murri, F. Sartucci. (2007). A Low-Cost Interface for Control of Computer Functions by Means of Eye Movements, Computers in Biology and Medicine, 37(12), 1765-1770. https://doi.org/10.1016/j.compbiomed.2007.05.003
  12. W.-D. Chang and J. Shin. (2009). Dynamic Positional Warping: Dynamic Time Warping for Online Handwriting. International Journal of Pattern Recognition and Artificial Intelligence, 23(5), 967-986. https://doi.org/10.1142/S0218001409007454
  13. W.-D. Chang and J. Shin. (2008) DPW Approach for Random Forgery Problem in Online Handwritten Signature Verification. The 4th International Conference on Networked Computing and Advanced Information Management, pp. 347-352. Gyeongju: IEEE.
  14. K. Yamagishi, J. Hori, M. Miyakawa. (2006). Development of EOG-based communication system controlled by eight-directional eye movements. International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 2574-2577. New York: IEEE.
  15. S. L. Wu, L. De Liao, S. W. Lu, W. L. Jiang, S.A. Chen, C.T. Lin. (2013). Controlling a human-computer interface system with a novel classification method that uses electrooculography signals, IEEE Transactions on Biomedical Engineering, 60, 2133-2141. https://doi.org/10.1109/TBME.2013.2248154
  16. A. Banerjee, S. Datta, M. Pal, A. Konar, D.N. Tibarewala, R. Janarthanan. (2013). Classifying Electrooculogram to Detect Directional Eye Movements, Procedia Technology, 10, 67-75. https://doi.org/10.1016/j.protcy.2013.12.338
  17. E. Ianez, J.M. Azorin, C. Perez-Vidal. (2013). Using Eye Movement to Control a Computer: A Design for a Lightweight Electro-Oculogram Electrode Array and Computer Interface, PLoS One, 8(7), Article ID: e67099.
  18. H.-J. Kim. (2017). A Review Study of Biosensors applicable to Wellness Wear, Journal of Digital Convergence, 15(11), 231-243. https://doi.org/10.14400/JDC.2017.15.11.231
  19. Y.-S. Jeong. (2017). Data Storage and Security Model for Mobile Healthcare Service based on IoT, Journal of Digital Convergence, 15(3), 187-193. https://doi.org/10.14400/JDC.2017.15.3.187
  20. M.-J. Lee, H.-K. Kang. (2017). Effects of Mobile based-Healthcare Service using Human Coaching to the Self-care of Diabetes. Journal of Convergence for Information Technology, 7(4), 83-89. https://doi.org/10.22156/CS4SMB.2017.7.4.083
  21. M.-G. Cho. (2017). Smart Elderly-care System using Smart-phone. Journal of Convergence for Information Technology, 7(5), 129-135. https://doi.org/10.14801/jaitc.2017.7.2.129