Mobility and low contrast trip hazard avoidance using augmented depth
Abstract
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 significantly 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.
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In this study, phosphenes were not modeled as pixels but as sparse dots distributed according to a Gaussian distribution.
“In this study, phosphenes were not modeled as pixels but as sparse dots distributed according to a Gaussian distribution.”
From Visual Prostheses in the Era of Artificial Intelligence Technology (2025) · cites this paper as [76]
Some phosphenes were removed (dropout) from the image to simulate malfunctioning electrodes.
“Some phosphenes were removed (dropout) from the image to simulate malfunctioning electrodes.”
From Visual Prostheses in the Era of Artificial Intelligence Technology (2025) · cites this paper as [76]
Other features included contrast information, semantic information, depth information, surface normal, convolutional filters, and the use of clip-art style images.
“Other features included contrast information,73,76 semantic information,80,82,83 depth information,76,79,81 surface normal,84 convolutional filters,73,75,77 and the use of clip-art style images.85,86”
From Visual Prostheses in the Era of Artificial Intelligence Technology (2025) · cites this paper as [76]
In an alternate approach, adaptive depth-contrast enhancement was proposed for improving obstacle detection in the path of a moving person, with validation of the saliency maps through performance of visual or mobility tasks.
“In an alternate approach, adaptive depth-contrast enhancement was proposed for improving obstacle detection in the path of a moving person,76 with validation of the saliency maps through performance of visual or mobility tasks.”
From Visual Prostheses in the Era of Artificial Intelligence Technology (2025) · cites this paper as [76]
The realism of simulated prosthetic vision models is frequently limited by an overall neglect of phosphene models, which are often confused with electrical stimulation protocols, or by using unrealistic simulation settings.
“However, the realism of these models is frequently limited by an overall neglect of phosphene models, which are often confused with electrical stimulation protocols,72 or by using unrealistic simulation settings.76,88”
From Visual Prostheses in the Era of Artificial Intelligence Technology (2025) · cites this paper as [76]
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“In an alternate approach, adaptive depth-contrast enhancement was proposed for improving obstacle detection in the path of a moving person, 76 with validation of the saliency maps through performance of visual or mobility tasks.”
- Authors
- McCarthy, C., Walker, J. G., Lieby, P., Scott, A., & Barnes, N.
- Affiliations
- Australian National University (AU)
- Data61 (AU)
- Corresponding author
- McCarthy, Chris
- Venue
- Journal of Neural Engineering
- Publisher
- IOP Publishing
- Published
- 2014-11-26
- Volume
- 12
- Issue
- 1
- Pages
- 016003
- ISSN
- 1741-2552 (ISSN-L)
- 1741-2560
- OpenAlex
- W2091865309
- Cited by
- 49
- References
- 58
- Citations by year
- 2026: 5 · 2025: 3 · 2024: 4 · 2023: 3 · 2022: 4 · 2021: 5 · 2020: 4 · 2019: 2 · 2018: 3 · 2017: 9 · 2016: 4 · 2015: 3
- FWCI
- 2.44
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Journal of Neural Engineering (publishedVersion)
- PubMed (publishedVersion)
- ANU Open Research (Australian National University) (submittedVersion)
- Swinburne figshare (Swinburne University of Technology) (submittedVersion)
- Funders
- Australian Research Council (Special Initiative in Bionic Vision, ICT Centre of Excellence program)
- Australian Government (Department of Broadband, Communicat)
- Topics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neural Engineering
- Life Sciences › Neuroscience › Cognitive Neuroscience › Tactile and Sensory Interactions
- Health Sciences › Health Professions › Occupational Therapy › Assistive Technology in Communication and Mobility
- Keywords
- prosthetic vision, visual prosthesis, phosphenes, collision avoidance
- MeSH terms
- Accidental Falls, Adult, Depth Perception, Gait, Humans, Image Enhancement, Male, Middle Aged, Phosphenes, Psychomotor Performance, Visual Prosthesis
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