Performance of complex visual tasks using simulated prosthetic vision via augmented-reality glasses
Performance of complex visual tasks using simulated prosthetic vision via augmented-reality glasses
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DOI：
10.1167/19.13.22
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发表时间：
2019-11-01
影响因子：
1.8
通讯作者：
Palanker, Daniel
中科院分区：
文献类型：
--
作者：
Ho, Elton;Boffa, Jack;Palanker, Daniel
光伏视网膜下假体设计用于恢复年龄相关性黄斑变性（AMD）患者的中心视力。我们研究了使用增强现实（AR）眼镜模拟降低的敏锐度，对比度和视野的复杂视觉任务的修复中心视觉的实用性。具有阻挡中心20度视野的AR眼镜包括集成的摄像机和软件，该软件根据三个用户定义的参数调整图像质量：分辨率，对应于植入物的等效像素大小;视野，对应于植入物大小;和灰度级数量。实时处理的视频在右眼前方的屏幕上播放。19名健康的参与者被招募来完成视觉任务，包括视觉图表，句子阅读和面部识别。视力表，字母敏锐度超过像素采样限制0.2 logMAR。阅读速度随着像素大小的增加和视场的减小（7度-12度）而降低。在面部识别任务（四路强迫选择，5度角大小）参与者识别的脸在>75%的准确性，即使有100 μ m像素和只有两个灰度级。在60 μ m像素和8个灰度级下，准确率超过97%。受试者与模拟假肢视力表现略好于抽样限制的字母敏锐度的任务，并高度准确地识别人脸，即使与100 μ m/像素的分辨率。这些结果表明，即使在100 μ m像素的阅读和面部识别使用假体中央视觉的可行性，性能进一步提高与更小的像素。
Photovoltaic subretinal prosthesis is designed for restoration of central vision in patients with age-related macular degeneration (AMD). We investigated the utility of prosthetic central vision for complex visual tasks using augmented-reality (AR) glasses simulating reduced acuity, contrast, and visual field. AR glasses with blocked central 20 degrees of visual field included an integrated video camera and software which adjusts the image quality according to three user-defined parameters: resolution, corresponding to the equivalent pixel size of an implant; field of view, corresponding to the implant size; and number of grayscale levels. The real-time processed video was streamed on a screen in front of the right eye. Nineteen healthy participants were recruited to complete visual tasks including vision charts, sentence reading, and face recognition. With vision charts, letter acuity exceeded the pixel-sampling limit by 0.2 logMAR. Reading speed decreased with increasing pixel size and with reduced field of view (7 degrees-12 degrees). In the face recognition task (four-way forced choice, 5 degrees angular size) participants identified faces at >75% accuracy, even with 100 mu m pixels and only two grayscale levels. With 60 mu m pixels and eight grayscale levels, the accuracy exceeded 97%. Subjects with simulated prosthetic vision performed slightly better than the sampling limit on the letter acuity tasks, and were highly accurate at recognizing faces, even with 100 mu m/pixel resolution. These results indicate feasibility of reading and face recognition using prosthetic central vision even with 100 mu m pixels, and performance improves further with smaller pixels.