Abstract
In this paper, an electrooculography (EOG)-based eye movement angle estimation approach, including signal acquisition, pre-processing, outlier removal and modeling, is proposed. The eye movement angle estimation model is a data-driven approach that using a non-linear polynomial method. It offers a simple, analytical, accurate, and cost-effective solution for real-time and large-space eye movement angle estimation. The feasibility of the proposed model was validated on a realistic scenario across 18 subjects. Experimental results show the horizontal estimation error in angle is less than 3.5°. Compared with most of the existing methods with high computational complexity, the proposed model can provide comparable results with less computational consumption cost in a large-space eye movement angle estimation. Meanwhile, the proposed model can be easily deployed in the embedded platform or mobile device with limited computing power and limited storage space for real-time eye movement angle estimation.
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References
Heide, W., et al.: Electrooculography: technical standards and applications. The international federation of clinical neurophysiology. Electroencephalogr. Clin. Neurophysiol. Suppl. 52, 223–240 (1999)
Barea, R., et al.: Wheelchair guidance strategies using EOG. J. Intell. Robot. Syst. 34(3), 279–299 (2002)
Deng, L.Y., et al.: EOG-based human–computer interface system development. Expert Syst. Appl. 37(4), 3337–3343 (2010)
Barbara, N., Camilleri, T.A., Camilleri, K.P.: EOG-based eye movement detection and gaze estimation for an asynchronous virtual keyboard. Biomed. Signal Process. Control 47, 159–167 (2019)
Zhang, Y.-F., et al.: A novel approach to driving fatigue detection using forehead EOG. In: 2015 7th International IEEE/EMBS Conference on Neural Engineering (NER). IEEE (2015)
Barbara, N., Camilleri, T.A., Camilleri, K.P.: Eog-based gaze angle estimation using a battery model of the eye. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE (2019)
Shinomiya, K., Shiota, H., Ohgi, Y., et al.: Analysis of the characteristics of electrooculogram applied a battery model to the eyeball. In: 2006 International Conference on Biomedical and Pharmaceutical Engineering, pp. 428–431. IEEE (2006)
Kumar, D., Poole, E.: Classification of EOG for human computer interface. In: Proceedings of the Second Joint 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society Engineering in Medicine and Biology, vol. 1. IEEE (2002)
Manabe, H., Fukumoto, M., Yagi, T.: Automatic drift calibration for EOG-based gaze input interface. In: 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE (2013)
Naga, R., et al.: Denoising EOG signal using stationary wavelet transform. Measur. Sci. Rev. 12(2), 46 (2012)
Sadasivan, P.K., Narayana Dutt, D.: A non-linear estimation model for adaptive minimization of EOG artefacts from EEG signals. Int. J. Bio-Med. Comput. 36(3), 199–207 (1994). https://doi.org/10.1016/0020-7101(94)90055-8
Bulling, A., et al.: Eye movement analysis for activity recognition using electrooculography. IEEE Trans. Pattern Anal. Mach. Intell. 33(4), 741–753 (2011)
Manabe, H., Fukumoto, M., Yagi, T.: Direct gaze estimation based on nonlinearity of EOG. IEEE Trans. Biomed. Eng. 62(6), 1553–1562 (2015)
Li, L., Wen, Z., Wang, Z.: Outlier detection and correction during the process of groundwater lever monitoring base on Pauta criterion with self-learning and smooth processing. In: Zhang, L., Song, X., Wu, Y. (eds.) AsiaSim/SCS AutumnSim 2016. CCIS, vol. 643, pp. 497–503. Springer, Singapore (2016). https://doi.org/10.1007/978-981-10-2663-8_51
Leys, C., et al.: Detecting outliers: do not use standard deviation around the mean, use absolute deviation around the median. J. Exp. Soc. Psychol. 49(4), 764–766 (2013). https://doi.org/10.1016/j.jesp.2013.03.013
Barea, R., et al.: EOG-based eye movements codification for human computer interaction. Expert Syst. Appl. 39(3), 2677–2683 (2012). https://doi.org/10.1016/j.eswa.2011.08.123
Acknowledgment
This work was supported in part by Shanghai Municipal Science and Technology International R&D Collaboration Project (Grant No. 20510710500) in part by the National Natural Science Foundation of China under Grant No. 62001118, and in part by the Shanghai Committee of Science and Technology under Grant No. 20S31903900.
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Zhu, Y., Tao, L., Zeng, Z., Zhu, H., Chen, C., Chen, W. (2022). An Accurate and Cost-Effective Approach Towards Real-Time Eye Movement Angle Estimation. In: Gao, X., Jamalipour, A., Guo, L. (eds) Wireless Mobile Communication and Healthcare. MobiHealth 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 440. Springer, Cham. https://doi.org/10.1007/978-3-031-06368-8_10
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