HMD-based cover test system for the diagnosis of ocular misalignment

  • Noriyuki UchidaEmail author
  • Kayoko Takatuka
  • Hisaaki Yamaba
  • Atsushi Nakazawa
  • Masayuki Mukunoki
  • Naonobu Okazaki
Original Article


The diagnosis of ocular misalignment is difficult and needs examination by ophthalmologists and orthoptists. However, there are not enough qualified personnel to perform such diagnoses. The eye position check is in part systematized. With this check system, we can detect not only the symptoms but also the angle and the extent of strabismus. However, the types of strabismus that can be detected with this technique are limited to exotropia. The purpose of this study is to develop a simplified check system to screen at least the presence of strabismus apart from the type of strabismus or amount of ocular deviation. First, we digitalized the check process. Specifically, we digitized the elemental technology, i.e., the cover–uncover function, required for automation of the typical cover test for eye position check. Furthermore, we developed and implemented an abnormality determination process and evaluated the performance of the system through this experiment, the results of which indicated a higher detection capability than the conventional cover test performed by ophthalmologists and orthoptists.


Abnormality of eye position Cover-uncover test Digitalization of check process 3D glasses 



This research was supported by a Grant-in-Aid for Scientific Research (JP17H01736) from JSPS KAKENHI.


  1. 1.
    Hasebe S (2001) The basis for progress in eye position examination. New ophthalmology 18(9):1105–1110Google Scholar
  2. 2.
    Fukuda T (2014) Survey report of three year old eye exam. J Jpn Ophthalmol Assoc 85(3):296–300Google Scholar
  3. 3.
    Takahashi Y et al (2016) Study of the occlusion time in fusion removal eye position measurement using a gaze analyzer. In: 71st Folia Japonica de Ophthalmologica Clinica of Japanese Association for Strabismus and Amblyopia 9(3), pp 234–237Google Scholar
  4. 4.
    Usui C (2000) Hess screen test. Jpn Assoc Certif Orthop J 28:81–92CrossRefGoogle Scholar
  5. 5.
    Fujita H, Kido S, Hara T et al (2016) Current trend of CAD system including trend of AI” RSNA 2015 best reports by experts, vol 9, pp 32–35Google Scholar
  6. 6.
    Suzuki S, Xiaoyong Z, Takane Y et al (2017) Computer aided image diagnostic system using deep learning for breast cancer lesion detection. In: Academic lecture for the Institute of Measurement Automatic Control Society System Information Section, pp 804–809Google Scholar
  7. 7.
    Saitou N, Ichiji K, Xiaoyon Z et al (2017) Tracking method based on affine transformation of target tumor in X-ray moving images for lung cancer radiation therapy. In: Academic lecture for the Institute of Measurement Automatic Control Society System Information Section, pp 798–803Google Scholar
  8. 8.
    Uchida N, Takatuka K, Hinokuma K et al (2017) Toward the development of a simple eye position inspection system using 3D glasses. In: IEICE general conference papers H4-3, Japan, p 269Google Scholar
  9. 9.
    Uchida N, Takatuka K, Hinokuma K et al (2018) Automated cover–uncover test system using active LCD shutter glasses. In: 23rd international symposium on artificial life and robotics (AROB), JapanGoogle Scholar
  10. 10.
    Takatuka K, Uchida N, Hinokuma K et al (2018) Development of support system for eye position abnormality using 3D glasses. SICE system Information Department Academic Lecture Meeting, JapanGoogle Scholar
  11. 11.
    PLATO LCD shutter goggles. Accessed 25 Jan 2018
  12. 12.
    Translucent Technologies. Accessed 25 Jan 2018
  13. 13.
  14. 14.
    Kitagawa Y, Katou T, Wu H et al (2005) Condensation with eye-model for gaze estimation. Technical reports of IPSJ, vol 2005-CVIM-150, no. 88, pp. 17–24Google Scholar
  15. 15.
    Nitschke C, Nakazawa A, Takemura H (2011) Display-camera calibration using eye reflections and geometry constraints. Comput Vis Image Underst 115:835–853CrossRefGoogle Scholar
  16. 16.
    Nakazawa A, Nitschke C (2012) Point of gaze estimation through corneal surface reflection in an active illumination environment. In: European Conference on Computer Vision—ECCV 2012, pp 159–172Google Scholar
  17. 17.
    OpenCV: introduction to image analysis visualization of optical flow. Accessed 21 Jan 2018
  18. 18.
    The principle of the image processing algorithms optical flow estimation. Accessed 21 Jan 2018
  19. 19.
    Yamauchi Y, Yamashita T, Fujiyoshi H (2013) Detection of human bodies based on statistical learning method from images. IEICE Trans J96-D(9):2017–2040Google Scholar
  20. 20.
    Shibata S, Zhqiang WU, Yamamoto T (2017) A constitutional method on gaze estimation using particle filter and neural network. J Inst Ind Appl Eng 5(1):25–33Google Scholar
  21. 21.
    Woods AJ, Helliwell J (2011) A survey of 3D sync IR protocols. CMST 944:1–16Google Scholar
  22. 22.
    Handbook of orthoptic principles. Accessed Aug 2018
  23. 23.
    Gunner.Fameback (2003) Two-frame motion estimation based on polynomial expansion. In: Scandinavian conference on image analysis, image analysis, pp 363–370Google Scholar

Copyright information

© International Society of Artificial Life and Robotics (ISAROB) 2019

Authors and Affiliations

  • Noriyuki Uchida
    • 1
    • 3
    Email author
  • Kayoko Takatuka
    • 3
  • Hisaaki Yamaba
    • 3
  • Atsushi Nakazawa
    • 2
  • Masayuki Mukunoki
    • 3
  • Naonobu Okazaki
    • 3
  1. 1.Kyushu University of Health and WelfareNobeokaJapan
  2. 2.Kyoto UniversityKyotoJapan
  3. 3.University of MiyazakiMiyazakiJapan

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