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Gislin Dagnelie, Roksana Sadeghi, Arathy Kartha, Paul Gibson, Ryan Chamberlain, Louis Barrett, Kevin Kramer; Comparing alerts in a smart object finder for visual prosthesis wearers. Invest. Ophthalmol. Vis. Sci. 2022;63(7):4523 – F0310.
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© ARVO (1962-2015); The Authors (2016-present)
Visual prosthesis wearers' ability to locate people and objects in real-life environments is limited by low resolution and distracting information. Object recognition by a neural net can assist them, provided we can efficiently convey information about the location and nature of a desired object in the visual field.
Video from a head-worn camera was analyzed for the presence of selected objects by a TensorFlow SSD neural net trained on the COCO dataset, running on a Raspberry Pi processor in a control box worn by the subject. Subjects wore their Argus system and earbuds, and were either shown the raw Argus II video or given one of 3 prompts for objects in view: a flashing icon in the image, icon + binaural tone conveying direction and size/closeness, or icon + spoken object identity. Here we present data collected in Argus II users looking for and walking towards a person in our lab in one of 6 pre-selected random positions. Each position was tested twice, in random order, followed by a return trial to the starting location; a trial ended in reaching the target or time-out/incomplete. Prior to testing, subjects familiarized themselves with the size of the room and were given several practice trials on a person standing nearby, with each modality. Outcomes were success rate, number of steps, and time to completion.
With the native Argus II system, success rates varied from 25–38%; 95–99 s and 27–33 steps were required to reach the target. In the trials with prompts, these values were: 92 – 96%, 26–34 s and 9–17 steps (icon); 83 – 100%, 31–32 s and 12–15 steps (icon+tone); and 92 – 100%, 35–37 s and 9–18 steps (icon+voice). All changes in prompted trials relative to the native system were significant by 2-tailed t-test. Only for one subject was a difference found between prompts: Icon + tone resulted in faster completion than icon alone. Return trials showed a slight, but not significant, improvement over outbound trials. Subjects preferred using the system, but did not express a preference for a particular prompt.
Providing an Argus II user with a system that detects a desired object results in significant improvement of all measured performance aspects, but no evidence was found for advantage of a particular alert system.
This abstract was presented at the 2022 ARVO Annual Meeting, held in Denver, CO, May 1-4, 2022, and virtually.
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