TY - JOUR
T1 - Dyslexia Analysis and Diagnosis Based on Eye Movement
AU - Vaitheeshwari, R.
AU - Chen, Chih Hsuan
AU - Chung, Chia Ru
AU - Yang, Hsuan Yu
AU - Yeh, Shih Ching
AU - Wu, Eric Hsiao Kuang
AU - Kumar, Mukul
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Dyslexia is a complex reading disorder characterized by difficulties in accurate or fluent word recognition, poor spelling, and decoding abilities. These challenges are not due to intellectual, visual, or auditory deficits. The diagnosis of dyslexia is further complicated by symptom variability, influenced by cultural and personal factors. This study leverages Virtual Reality (VR) advancements, eye movement tracking, and machine learning to create a virtual reading environment that captures eye movement data. This data extracts features such as eye movement metrics, word vectors, and saliency maps. We introduce a novel fusion model that integrates various machine learning algorithms to objectively and automatically assess dyslexia using physiological data derived from user interactions. Our findings suggest that this model significantly enhances the accuracy and efficiency of dyslexia diagnosis, marking an important advancement in educational technology and providing robust support for individuals with dyslexia. Although the sample size was limited to 10 dyslexic and 4 control participants, the results offer valuable insights and lay the groundwork for future studies with larger cohorts.
AB - Dyslexia is a complex reading disorder characterized by difficulties in accurate or fluent word recognition, poor spelling, and decoding abilities. These challenges are not due to intellectual, visual, or auditory deficits. The diagnosis of dyslexia is further complicated by symptom variability, influenced by cultural and personal factors. This study leverages Virtual Reality (VR) advancements, eye movement tracking, and machine learning to create a virtual reading environment that captures eye movement data. This data extracts features such as eye movement metrics, word vectors, and saliency maps. We introduce a novel fusion model that integrates various machine learning algorithms to objectively and automatically assess dyslexia using physiological data derived from user interactions. Our findings suggest that this model significantly enhances the accuracy and efficiency of dyslexia diagnosis, marking an important advancement in educational technology and providing robust support for individuals with dyslexia. Although the sample size was limited to 10 dyslexic and 4 control participants, the results offer valuable insights and lay the groundwork for future studies with larger cohorts.
KW - Cognitive assessment
KW - diagnostic tools
KW - dyslexia
KW - eye movement tracking
KW - fusion models
KW - machine learning
KW - physiological data analysis
UR - http://www.scopus.com/inward/record.url?scp=85209741227&partnerID=8YFLogxK
U2 - 10.1109/TNSRE.2024.3496087
DO - 10.1109/TNSRE.2024.3496087
M3 - 期刊論文
C2 - 39527420
AN - SCOPUS:85209741227
SN - 1534-4320
VL - 32
SP - 4109
EP - 4119
JO - IEEE Transactions on Neural Systems and Rehabilitation Engineering
JF - IEEE Transactions on Neural Systems and Rehabilitation Engineering
ER -