2.2. Materials and methods 2 15 Camera Vision CED-based SPV SharpNet-based SPV 2 sessions x 2 sessions x 2 sessions x 2 environments x 2 environments x 2 environments x 1 simulation: 6 simulations: 2 simulations: • noSPV • 10×10 • 26×26 • 18×18 • 42×42 • 26×26 • 34×34 • 42×42 • 50×50 Total: 4 trials Total: 24 trials Total: 8 trials Table 2.2: Overview of study conditions and corresponding number of trials. CED: Canny edge detection; SPV: simulated prosthetic vision. attainable benefits of a stricter surface-boundary representation where within-surface gradients and background are removed, we compare the performance with Canny edge detection in the complex versus the plain visual environment. iii) To assess the feasibility of obtaining such strict scene simplification with real-time deep learning-based surface boundary detection, we tested SharpNet at two different phosphene resolutions in both the complex and plain visual environment. Here, the SharpNet model was evaluated against CED as a control condition. Based on the aforementioned literature on overcrowding, we hypothesize that mobility in the complex environment with a low phosphene resolution (such as 26 × 26 phosphenes), can be improved with deep learning-based scene simplification compared to basic image processing with Canny edge detection. An overview of the study conditions can be found inTable2.2. Note that a representative selection of these conditions was practiced in the training session (i.e., both low and high phosphene resolutions, both environmental complexities and both image processing methods). At the beginning of each trial, an auditory start-cue was presented to the participants. To encourage the maximal performance achievable, as limited by the visual input, instructions were to walk as fast as possible, whilst avoiding the obstacles. Between each trial, the obstacles were systematically shuffled to match one of seven pre-defined route layouts. 2.2.6. Randomization In an effort to minimize systematic bias due to learning effects, or due to characteristics of the route layout, both the order of all phosphene simulations and the order of the route layouts were randomized. For corresponding phosphene simulation conditions, the route layouts were matched but mirrored across the two different visual complexity conditions. Similarly, to allow for a clean comparison between the two image pre-processing methods, the route layouts were matched between the SharpNet and corresponding CED conditions. 2.2.7. Statistical analysis Statistical analysis was performed using the SciPy statistics toolbox (version 1.3.2) for Python(Virtanen et al., 2020). All three endpoint parameters were standardized within
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