Visual acuity (VA) measurement is utilized to test a subject’s acuteness of vision. Conventional VA measurement requires a physician’s assistance to ask a subject to speak out or wave a hand in response to the direction of an optotype. To avoid this repetitive testing procedure, different types of automatic VA tests have been developed in recent years by adopting contact-based responses, such as pushing buttons or keyboards on a device. However, contact-based testing is not as intuitive as speaking or waving hands, and it may distract the subjects from concentrating on the VA test. Moreover, problems related to hygiene may arise if all the subjects operate on the same testing device. To overcome these problems, we propose an intelligent VA estimation (iVAE) system for automatic VA measurements that assists the subject to respond in an intuitive, noncontact manner. VA estimation algorithms using maximum likelihood (VAML) are developed to automatically estimate the subject’s vision by compromising between a prespecified logistic function and a machine-learning technique. The neural-network model adapts human learning behavior to consider the accuracy of recognizing the optotype as well as the reaction time of the subject. Furthermore, a velocity-based hand motion recognition algorithm is adopted to classify hand motion data, collected by a sensing device, into one of the four optotype directions. Realistic experiments show that the proposed iVAE system outperforms the conventional line-by-line testing method as it is approximately ten times faster in testing trials while achieving a logarithm of the minimum angle of resolution error of less than 0.2. We believe that our proposed system provides a method for accurate and fast noncontact automatic VA testing.

中文翻译：

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具有手部动作识别功能的智能视力评估系统


视力（VA）测量用于测试受试者的视力敏锐度。传统的视力测量需要医生的帮助，要求受试者根据视标的方向说话或挥手。为了避免这种重复的测试过程，近年来通过采用基于接触的响应（例如按下设备上的按钮或键盘）开发了不同类型的自动 VA 测试。然而，基于接触的测试不像说话或挥手那样直观，它可能会分散受试者对 VA 测试的注意力。此外，如果所有受试者在同一测试设备上进行操作，可能会出现与卫生相关的问题。为了克服这些问题，我们提出了一种用于自动 VA 测量的智能 VA 估计（iVAE）系统，可帮助受试者以直观、非接触的方式做出反应。使用最大似然 (VAML) 的 VA 估计算法被开发出来，通过在预先指定的逻辑函数和机器学习技术之间进行折衷来自动估计受试者的视力。神经网络模型适应人类的学习行为，考虑识别视标的准确性以及受试者的反应时间。此外，采用基于速度的手部动作识别算法将传感设备收集的手部动作数据分类为四个视标方向之一。实际实验表明，所提出的 iVAE 系统优于传统的逐行测试方法，因为它在测试试验中快了约十倍，同时实现了小于 0.2 的最小角度分辨率误差的对数。我们相信，我们提出的系统提供了一种准确、快速的非接触式自动 VA 测试方法。