Real and virtual mobility performance in simulated prosthetic vision
- PMID: 17325421
- DOI: 10.1088/1741-2560/4/1/S11
Real and virtual mobility performance in simulated prosthetic vision
Abstract
Wayfinding is an important activity that can be performed with limited visual resources, and thus may be an important application of early visual prostheses. In a pair of experiments we explored minimal visual resolution requirements of a simulated retinal electrode array for mobility in real and virtual environments, experienced by normally sighted subjects in video headsets. In experiment 1, inexperienced and experienced subjects traveled similar routes around a suite of offices with simulated implants of 4 x 4, 6 x 10 and 16 x 16 dots. In experiment 2, the effects of adding dynamic noise and removing a subset of 'phosphenes' from a 6 x 10 dot array on the mobility of experienced subjects through a series of different virtual 10-room buildings were determined. Performance was quantified in terms of time and navigation errors in both experiments, and wall contacts in the real environment; a compound score was also computed for trials in experiment 1. In experiment 1, inexperienced subjects required 16 x 16 dots for adequate performance, while experienced subjects reached similar levels with 6 x 10 dots. In experiment 2, dot removal up to 30% led to modest yet significant performance deterioration, and noise addition to slight but non-significant improvement, while practice led to a reduction in travel time by 50% over the 28-trial experiment. Error counts in experiment 2 were fairly high, but largely randomly distributed, and attributable to the high risk of becoming disoriented in the sparse visual environment. Substantial performance level differences were found between subjects, spanning a threefold range even after practice. The findings suggest that a retinal implant with as few as 60 electrodes may provide independent wayfinding abilities to the adventitiously blind, but that substantial practice and supervision will be required in learning this task.
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