Stahl Michael, Peli Eli
Schepens Eye Research Institute of Mass. Eye and Ear, Harvard Medical School, Boston, MA, USA.
Northeastern University, Department of Bioengineering, Boston, MA, USA.
Transl Vis Sci Technol. 2020 Aug 4;9(9):6. doi: 10.1167/tvst.9.9.6. eCollection 2020 Aug.
Using a geometrically derived model and a virtual curb simulator, we quantify the degree to which a wearable device that projects a laser line onto tripping hazards in a pedestrian's path improves visual recognition for people with visual impairments (VI). We confirm this with subjects' performance on computer simulations of low contrast curbs.
We derive geometric expressions quantifying the visual cue users perceive when a single laser line is projected from their hip onto a curb. We show how the efficacy of this cue changes with the angle of the laser line relative to the subject's walking trajectory. We confirm this result with data from three subjects with VI in a simulated curb recognition task in which subjects classified computer images as an "Ascending," "Flat," or "Descending" curb.
The derived model predicts that human recognition performance depends strongly on the laser line angle and the subject data confirms this ( = 0.86 < 0.001). The laser line cue improved subject accuracy from a chance level of 33% to 95% for a simulated, one-inch, low-contrast curb at a distance of five feet.
Recognition of curbs in low light can be improved by augmenting the scene with a single laser line projected from a user's hip, if the angle of laser line is appropriately selected.
A majority of people with VI rely on their impaired residual vision for mobility, rather than a mobility aid, resulting in increased injury for this population. Enhancing residual vision could promote safety, increase independence, and reduce medical costs.
使用一个基于几何原理的模型和一个虚拟路缘模拟器，我们量化了一种可穿戴设备将激光线投射到行人路径上的绊倒危险物上时，对视力障碍者（VI）视觉识别能力的提升程度。我们通过受试者在低对比度路缘计算机模拟中的表现来证实这一点。
我们推导了量化视觉线索的几何表达式，即当一条激光线从使用者的臀部投射到路缘上时，使用者所感知到的视觉线索。我们展示了这种线索的效果如何随着激光线相对于受试者行走轨迹的角度而变化。我们在一项模拟路缘识别任务中，用来自三名视力障碍受试者的数据证实了这一结果，在该任务中，受试者将计算机图像分类为“上升”、“平坦”或“下降”路缘。
推导的模型预测，人类识别性能在很大程度上取决于激光线角度，受试者数据证实了这一点（ = 0.86 < 0.001）。对于模拟的、一英寸高、低对比度、距离为五英尺的路缘，激光线索将受试者的准确率从33%的随机水平提高到了95%。
如果适当选择激光线的角度，通过从使用者臀部投射一条激光线来增强场景，可以提高在低光照下路缘的识别能力。
大多数视力障碍者依靠其受损的残余视力进行移动，而不是依靠移动辅助工具，这导致该人群受伤风险增加。增强残余视力可以提高安全性、增加独立性并降低医疗成本。
