Eye Movement Analysis using Virtual Reality in Early Detection of Alzheimer’s Disease

Early detection of Alzheimer’s disease (AD) is important at the initial stage to provide prior treatment and control. In traditional diagnostic methods, medical doctors perform manual AD checking by asking patients to repeat several round of text reading and game exercises. The quantification of this manual assessment can be subjective due to the profession experience and it requires a further verification using the brain imaging. In this paper, an automated digital AD assessment method using Virtual Reality (VR) console is proposed to evaluate patient’s condition by tracking their eye movement during text reading. First, the eye movement pattern is captured using VR console during text reading. The recorded eye movement is then converted into frequency domain to study its frequency components during reading process. The simulation of AD symptom is set up by prompting subjects to read their familiar native language and unfamiliar language. As a result, the experiment shows that there are significant differences in eye movement patterns between native and unfamiliar language reading. The group of subjects reading unfamiliar language have random eye movement in less efficient visual processing. This pattern is similar to the patients who suffer in the AD symptom. In addition, the frequency features are extracted to study its correlations with reading comprehension performance in order to detect the early AD condition.