| Summary: | 碩士 === 國立交通大學 === 電信工程研究所 === 103 === Visual acuity (VA) measurement is for a subject to test his/her acuteness of vision. Traditional VA measurement includes physician’s assistance, which can be surely replaced by machine since the whole procedure is uncomplicated but repetitious. Therefore, several kinds of automatic VA test are gradually developed and used in recent years. Without experimenter, the traditional way for a subject to speak out or wave a hand in response to the direction of optotype is then replaced mostly by the contact based response such as pushing buttons or keyboards on a device nowadays. However, the contact based response is not intuitive as speaking or waving hands, and it may distract subjects from concentrating on the test. Moreover, the hygienic problem may appear if all subjects operate on the same device. To overcome these problems, we propose an intelligent visual acuity estimation system (iVAE) which keeps the advantage of automatic VA measurement, and also allows subject to respond in an intuitive non-contact way. A velocity based hand motion recognition (V-HMR) algorithm is used to classify hand motion data collected by a sensing device into one of the four directions of optotype. Based on the V-HMR scheme, a visual acuity estimation algorithm using maximum likelihood (VAML) is developed for estimating subject’s vision and is implemented on a tablet. Three sub-schemes of VAML using different likelihood functions is proposed. A supervised machine learning technique, Neural Network, is used for learning human behavior when subject recognizes the optotype. Different response attributes could be considered and implicit characteristic could be found. According to the experimental results, we can conclude that the proposed iVAE system achieve our goals to provide accurate and efficient automatic VA measurements.
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