June 2021
Volume 62, Issue 8
Open Access
ARVO Annual Meeting Abstract  |   June 2021
Object Rankings and Notification Preferences for an Object Finder System among People with Ultra-Low Vision
Author Affiliations & Notes
  • Arathy Kartha
    Ophthalmology, Johns Hopkins Medicine, Baltimore, Maryland, United States
  • Roksana Sadeghi
    Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States
  • Nikki Singh
    Johns Hopkins University, Baltimore, Maryland, United States
  • Suyeon Ju
    Johns Hopkins University, Baltimore, Maryland, United States
  • Ryan Chamberlain
    Minnesota Health Solutions, Minnesota, United States
  • Kevin Kramer
    Minnesota Health Solutions, Minnesota, United States
  • Paul Gibson
    Advanced Medical Electronics, Minnesota, United States
  • Soo Hyun Lee
    Ophthalmology, Johns Hopkins Medicine, Baltimore, Maryland, United States
  • Gislin Dagnelie
    Ophthalmology, Johns Hopkins Medicine, Baltimore, Maryland, United States
  • Footnotes
    Commercial Relationships   Arathy Kartha, None; Roksana Sadeghi, None; Nikki Singh, None; Suyeon Ju, None; Ryan Chamberlain, None; Kevin Kramer, None; Paul Gibson, None; Soo Hyun Lee, None; Gislin Dagnelie, None
  • Footnotes
    Support  R44 EY027650
Investigative Ophthalmology & Visual Science June 2021, Vol.62, 3581. doi:
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      Arathy Kartha, Roksana Sadeghi, Nikki Singh, Suyeon Ju, Ryan Chamberlain, Kevin Kramer, Paul Gibson, Soo Hyun Lee, Gislin Dagnelie; Object Rankings and Notification Preferences for an Object Finder System among People with Ultra-Low Vision. Invest. Ophthalmol. Vis. Sci. 2021;62(8):3581.

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

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Abstract

Purpose : To develop a list of everyday items based on the Common Objects in Context (COCO) dataset, that are highly useful among people with native and artificial ultra-low vision (ULV) so that successful computer vision algorithms can be developed for object identification.

Methods : 10 participants with ULV (VA 20/1600) and 5 Argus II users were given 150 items from the COCO dataset and asked to rate the objects as very useful, useful, neutral, not useful, definitely not useful and the scores were 2, 1, 0, -1, -2 respectively. They were also asked about their preferences for system notifications (visual, speech, sound and haptic) for passive (general scanning) and active modes (scanning for a specific object).

Results : Participants with native ULV and Argus II users rated more than 50% of the items on the dataset as useful and very useful. The objects that were reported to be most useful were similar in both groups, the top 5 useful objects reported that would benefit from using an object finder device were phone, empty seat, remote control, person and toilet. The category that was rated the highest was person and the category with the lowest rating was animal (Fig 1). All participants preferred speech and sound notifications compared to visual or haptic notifications for both passive and active modes irrespective of their level of residual vision (between 1.4 and 3.5 log MAR).

Conclusions : We developed a priority list of useful objects from everyday life based on the COCO dataset among people with native ULV and Argus II users. This list of objects will be used to develop an object finder system using machine learning algorithms for people with ULV. Using the subjective ratings from this study, performance measures will be developed to determine the effect of an object finder system on functional performance and accessibility in people with ULV and visual prosthesis. Future studies will compare the effect of different modalities of notifications on functional performance in people with ULV and visual prosthesis.

This is a 2021 ARVO Annual Meeting abstract.

 

Figure 1. Average usefulness scores by category. Error bars represent standard error of the means.

Figure 1. Average usefulness scores by category. Error bars represent standard error of the means.

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