Development of an AI Assistant for Individuals with Low Vision and Blindness

为低视力和失明人士开发人工智能助手

• 批准号: 10615838
• 负责人: David M. Rosler
• 金额: --
• 依托单位: PROVIDENCE VA MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10615838
• 关键词: 3-Dimensional; Address; Age; Algorithms; Architecture; Artificial Intelligence; Auditory; Automobiles; Blindness; Canes; Cellular Phone; Central Scotomas; Clothing; Color; Complete Blindness; Computer Vision Systems; Computer software; Computers; Cues; Development; Device or Instrument Development; Devices; Diabetic Retinopathy; Effectiveness; Electrical Stimulation of the Brain; Electrodes; Environment; Eyeglasses; Face; Feedback; Focus Groups; Glaucoma; Goals; Hand; Head; Healthcare Systems; Home; Implant; Individual; Instruction; Intelligence; Internet; Interview; Intuition; Language; Learning; Legal Blindness; Light; Location; Machine Learning; Macular degeneration; Microcomputers; Names; National Eye Institute; Operative Surgical Procedures; Paper; Perception; Performance; Peripheral; Persons; Quality of life; Reader; Research; Residual state; Resolution; Retina; Retinitis Pigmentosa; Robotics; Self-Help Devices; Software Engineering; Specific qualifier value; System; Tactile; Technology; Terrorism; Testing; Text; Training; Unemployment; United States; Universities; Veterans; Video Games; Vision; Vision Disorders; Visual; Visual Cortex; Visual impairment; Visually Impaired Persons; blind; communication device; convolutional neural network; cost; detector; effective therapy; experience; grasp; improved; interest; legally blind; neural network; novel; object recognition; open source; optical character recognition; prototype; rehabilitation service; sensor; sight restoration; smartphone application; social; sound; speech recognition; surgical risk; system architecture; voice recognition

The National Eye Institute estimates that in the United States there are presently over 1 million legally blind people and this number will increase to about 4 million by 2050. According to the VA Office of Blind Rehabilitation Services, there are approximately 130,000 US Veterans who are legally blind and more than one million who have lost the ability to perform daily tasks because of low vision. Some of these people can be helped by relatively simple and inexpensive devices such as magnifiers and canes. However, with more severe vision loss, people are likely to experience loss of independence and reduced quality of life. Blindness has a major impact on Veterans and others, as evidenced by the fact that over 70% of working-age people with significant visual impairment are unemployed. While there are causes of blindness that can be readily corrected, in many cases there is no effective treatment. One solution is to restore vision by surgically placing electrodes in the retina or visual cortex and electrically stimulating the brain based on the light level recorded by a head-mounted camera. Systems of this type are being developed and they have great potential. However, there are downsides to the implanted systems that include the risks of surgery, high cost, and a form of vision that is severely limited compared to normal perception. There is also a wide array of devices and smartphone apps that perform specialized functions (text readers, obstacle detectors, color identifiers). The goal of the proposed research is to develop and test a new type of visual assistive device, named AEyes, for individuals with blindness and low vision. It takes advantage of recent advances in artificial intelligence including computer vision, machine learning, optical character recognition, speech recognition, and 3D sound rendering to give the user the ability to recognize, localize, and interact with objects and people in their vicinity. In other words, the AI technology that can benefit people with low vision already exists and it will be implemented in a device tailored to the needs of people with visual disorders. The system will be intuitive and easy to learn and interact with as it understands spoken instructions and it speaks to the user. AEyes has been developed based on a needs-analysis and feedback from focus groups, one-on-one interviews, and prototype tests with Veterans conducted at the VA Providence Healthcare System. Aim 1 concerns device development. The system architecture and algorithms are largely implemented and functional, but a range of refinements to the software and the integration of separate functional modules into the overall system are needed. These improvements include upgrading to a higher resolution camera, retraining the recognition neural network, and integrating the face recognition, hand tracking, and voice recognition modules into the central architecture. Aim 2 consists of device tests that will be conducted with subjects having a range of low vision conditions that include no light sense, retinitis pigmentosa, glaucoma, macular degeneration, and diabetic retinopathy. The tests to be conducted include the ability of a user to recognize and localize people, reach for and grasp objects, read text on handheld paper, computer displays, and signs, and train the system to recognize new people. Comparing performance with residual vision alone and with the addition of AEyes will establish the device’s effectiveness as a visual assistant and indicate where improvements are needed.

国家眼科研究所估计，在美国，合法占有超过100万 盲人和这个数字到2050年将增加到约400万。 康复服务，大约有130,000名美国退伍军人在法律上是盲目的，一个以上 数百万因低视力而失去了执行日常任务的能力。其中一些人可以 通过相对简单且廉价的设备（例如放大镜和拐杖）的帮助。但是，更严重 视力丧失，人们可能会遭受独立丧失和生活质量的降低。失明有一个 对退伍军人和其他人的重大影响，这一事实证明了超过70％的工作年龄人 重大视觉障碍失业。 尽管有可能很容易纠正失明的原因，但在许多情况下没有有效 治疗。一种解决方案是通过手术将电子放入视网膜或视觉皮层和视觉上来恢复视力 根据头部安装摄像头记录的光级刺激大脑。系统的系统 正在开发类型，它们具有巨大的潜力。但是，植入有缺点 与相比 正常的感知。还有各种各样的设备和智能手机应用程序可以执行专业 功能（文本读取器，障碍物，颜色标识符）。 拟议的研究的目的是开发和测试一种新型的视觉辅助设备，命名为 AEYES，适用于失明和低视力的人。它利用了人造的最新进展 智能包括计算机视觉，机器学习，光学特征识别，语音识别和 3D声音呈现，使用户能够识别，本地化和与对象和人员进行识别和互动 他们的附近。换句话说，可以使低视力的人受益的AI技术已经存在，它将 在根据视觉障碍者的需求量身定制的设备中实施。该系统将是直观的 并且易于学习和互动，因为它可以理解口语说明，并且与用户说话。 Aeyes有 是根据焦点小组的需求分析和反馈而开发的，一对一的访谈和 对VA Providence Healthcare系统进行的退伍军人的原型测试。 AIM 1关注设备开发。系统体系结构和算法在很大程度上实施 和功能，但对软件进行了一系列改进以及单独的功能模块的集成 需要进入整体系统。这些改进包括升级到高分辨率摄像头， 重新训练识别神经元网络，并集成面部识别，手动跟踪和声音 识别模块进入中央建筑。 AIM 2由设备测试组成，这些测试将与具有一系列低视力的受试者进行 没有光感，色素性视网膜炎，青光眼，黄斑变性和糖尿病的疾病 视网膜病。要进行的测试包括用户识别和本地化的能力， 并抓住对象，阅读手持纸上的文本，计算机显示和符号，然后训练系统 认可新朋友。仅将表现与单独的残留视力进行比较，并与AEYES的添加将 建立设备作为视觉助手的有效性，并指示需要改进的地方。