2 16 2. Real-world indoor mobility with simulated prosthetic vision Figure 2.4: Image processing and phosphene simulation in the plain environment. A) Input image B) Edge mask, produced using the Canny algorithm. C) Surface boundary mask produced using the SharpNet predictions D-F) Comparison of different simulated phosphene resoutions (10 × 10, 26 × 26 and 50 × 50 phosphenes, respectively), with activations based on Canny edge mask. participants (i.e., the mean was subtracted and results are divided by the standard deviation) to reduce variance caused by inter-individual differences in walking speed, avoidance strategy and subjective experience. The endpoint parameters were found to be non-normally distributed across participants, as assessed with the Shapiro–Wilk test. Statistical hypothesis testing was performed using the Wilcoxon signed-rank test. Alpha was set at 0.05 and adjusted with the Bonferroni method for multiple planned comparisons. Six tests were performed to assess the effect of scene complexity with CED SPV at each phosphene resolution. Four tests were performed to compare surface boundary detection with SharpNet against edge detection with CED in each sub-condition that was measured (i.e., two phosphene resolutions and two scene complexities). 2.3. Results 2.3.1. General results SeeTable2.3for descriptive statistics of the obtained data. We found a small but significant negative correlation between the trial duration and the trial number (Pearson’s R = -0.15, p < 0.001). On average SPV trials in the second session were performed 3.468 seconds faster compared to the first session. No learning effects were found for number of collisions and subjective rating. The average performance varied across participants with a standard deviation of 7.748 seconds for the trial duration; 0.251 for number of collisions; and 0.783 for subjective rating. Regression analysis and subgroup analysis of the average
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