A Mixed Reality System for Modeling Perceptual Deficit to Correct Neural Errors and Recover Functional Vision
Autor: | Nasif Zaman, Alireza Tavakkoli, Stewart Lee Zuckerbrod |
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Rok vydání: | 2020 |
Předmět: |
Rehabilitation
genetic structures Computer science business.industry media_common.quotation_subject medicine.medical_treatment Macular degeneration medicine.disease eye diseases Mixed reality Visualization Visual field Perception Human visual system model medicine Computer vision Vision test Artificial intelligence business media_common |
Zdroj: | VR Workshops |
DOI: | 10.1109/vrw50115.2020.00055 |
Popis: | Several neuroocular pathologies, from Age-related Macular Degeneration (AMD) to the most recently discovered Spaceflight Associated Neuroocular Syndrome (SANS), cause neural vision errors ranging from mild scotomas to irreversible vision loss. The complexity of the human visual system and many accommodations and affordances within its numerous pathways have made it difficult to study perceptual deficits due to damaged neural structure of the visual system. This barrier prevents the development of effective treatments or rehabilitation regimes to help patients recover their functional vision. In this project we propose a novel computational and mathematical system enabled by advances in Virtual/Augmented/Mixed Reality (VAMR) that allows patients recover visual function. The proposed system enables patients suffering from neuroocular damage establish a parametric model of their perceptual deficit through a battery of vision tests administered by a Head-Mounted Display (HMD). This model is applied to modify the image in the scotomatous region of the affected eye to invoke binocular interactions to compensate for the visual deficit. In order to establish the viability of binocular interactions (i.e., suppression), we conducted a small preliminary study with healthy controls. The results show that healthy subjects when presented with scotomas in their visual field recover visual function by utilizing the proposed system. |
Databáze: | OpenAIRE |
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