June 2017
Volume 58, Issue 8
Open Access
ARVO Annual Meeting Abstract  |   June 2017
eyeFusion: Mobile, Objective Quantification of Critical Flicker Fusion Thresholds
Author Affiliations & Notes
  • Kasra Zarei
    Biomedical Engineering, University of Iowa, Coralville, Iowa, United States
    Ophthalmology and Visual Sciences, University of Iowa, Iowa City, Iowa, United States
  • Pieter Poolman
    Center for the Prevention and Treatment of Visual Loss, Veterans Affairs (VA) Health Care System, University of Iowa, Iowa City, Iowa, United States
    Ophthalmology and Visual Sciences, University of Iowa, Iowa City, Iowa, United States
  • Mona K Garvin
    Center for the Prevention and Treatment of Visual Loss, Veterans Affairs (VA) Health Care System, University of Iowa, Iowa City, Iowa, United States
    Ophthalmology and Visual Sciences, University of Iowa, Iowa City, Iowa, United States
  • Randy Kardon
    Ophthalmology and Visual Sciences, University of Iowa, Iowa City, Iowa, United States
    Center for the Prevention and Treatment of Visual Loss, Veterans Affairs (VA) Health Care System, University of Iowa, Iowa City, Iowa, United States
  • Footnotes
    Commercial Relationships   Kasra Zarei, None; Pieter Poolman, None; Mona Garvin, None; Randy Kardon, None
  • Footnotes
    Support  None
Investigative Ophthalmology & Visual Science June 2017, Vol.58, 3129. doi:
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      Kasra Zarei, Pieter Poolman, Mona K Garvin, Randy Kardon; eyeFusion: Mobile, Objective Quantification of Critical Flicker Fusion Thresholds. Invest. Ophthalmol. Vis. Sci. 2017;58(8):3129.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : To present a novel, mobile-phone application (eyeFusion) that can objectively quantify critical flicker fusion (CFF) thresholds, within the confines of smartphone hardware limitations. CFF has long been established as a sensitive measure of a visual conduction time, which becomes abnormal in retinal and optic nerve disorders.

Methods : The testing paradigm implemented (Fig. 1) consists of a ring of twelve circular stimuli (0.3 inches in diameter, which subtends 1.56 degrees visual angle for the diameter of each circular stimulus) at different grayscale contrasts, randomized in location, each centered on a median grayscale intensity background. Nine stimuli oscillate at specified temporal frequencies, while the remaining three do not flicker and are presented at zero percent contrast. The objective of the user is to tap each of the stimuli that appear to flicker, which then eliminates them from the screen. A photodiode was used to measure the true temporal frequency presented at programmed temporal frequencies of 1, 7.5, 15, and 30 Hz on the iPhone 5c, iTouch 5, and iPad Air. The discrete Fourier transform was computed to characterize the frequency response of each measured signal. Each signal was fitted for a square wave, with the correlation coefficients measured.

Results : For the 1, 7.5, 15, and 30 Hertz signals respectively, the dominant peak frequencies computed were 0.98, 7.49, 15, and 30.01 Hz on the iPhone, 0.98, 7.50, 14.99, and 29.98 Hz on the iTouch, and 0.99, 7.50, 15, and 30 Hz on the iPad. Correlations of the 1 Hz signals with fitted square wave were r2 = 0.98 for all three devices. For the iPhone, iTouch, and iPad respectively, r2 for the 7.5 Hz signals were 0.87, 0.84, and 0.85; 0.72, 0.65, and 0.64 for the 15 Hz signals, and 0.46, 0.35, and 0.42 for the 30 Hz signals. Furthermore, 30 Hz flicker stimuli on average only reached 90.6% of the value of the intended contrast.

Conclusions : These results demonstrate the technical feasibility of flicker fusion testing with smartphone and tablet devices. Hardware limitations restrict the temporal frequencies that can be presented. 30 Hz oscillations have reduced accuracy in the temporal and intensity domain of pixel presentation. With confirmation that a robust signal can be presented at 7.5 Hz, eyeFusion provides a useful approach to efficiently quantify flicker fusion thresholds on large populations.

This is an abstract that was submitted for the 2017 ARVO Annual Meeting, held in Baltimore, MD, May 7-11, 2017.

 

Figure 1: User Interface of the eyeFusion application

Figure 1: User Interface of the eyeFusion application

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