Mobility and low contrast trip hazard avoidance using augmented depth

Chris McCarthy, Janine G. Walker, Paulette Lieby, Adele Scott, Nick Barnes

    Research output: Contribution to journalArticlepeer-review

    37 Citations (Scopus)


    Objective. We evaluated a novel visual representation for current and near-term prosthetic vision. Augmented depth emphasizes ground obstacles and floor-wall boundaries in a depth-based visual representation. This is achieved by artificially increasing contrast between obstacles and the ground surface via a novel ground plane extraction algorithm specifically designed to preserve low-contrast ground-surface boundaries. Approach. The effectiveness of augmented depth was examined in human mobility trials compared against standard intensity-based (Intensity), depth-based (Depth) and random (Random) visual representations. Eight participants with normal vision used simulated prosthetic vision with 20 phosphenes and eight perceivable brightness levels to traverse a course with randomly placed small and low-contrast obstacles on the ground. Main results. The number of collisions was signi ficantly reduced using augmented depth, compared with intensity, depth and random representations (48%, 44% and 72% less collisions, respectively). Significance. These results indicate that augmented depth may enable safe mobility in the presence of low-contrast obstacles with current and near-term implants. This is the first demonstration that an augmentation of the scene ensuring key objects are visible may provide better outcomes for prosthetic vision.

    Original languageEnglish
    Article number016003
    JournalJournal of Neural Engineering
    Issue number1
    Publication statusPublished - 1 Feb 2015


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