Developmental dyslexia detection using machine learning techniques : A survey

被引:28
作者
Kaisar, Shahriar [1 ]
机构
[1] RMIT Univ, Sch Business IT & Logist, Melbourne, Vic, Australia
关键词
Dyslexia; Machine learning; Survey; EEG;
D O I
10.1016/j.icte.2020.05.006
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Developmental dyslexia is a learning disability that occurs mostly in children during their early childhood. Dyslexic children face difficulties while reading, spelling and writing words despite having average or above-average intelligence. As a consequence, dyslexic children often suffer from negative feelings, such as low self-esteem, frustration, and anger. Therefore, early detection of dyslexia is very important to support dyslexic children right from the start. Researchers have proposed a wide range of techniques to detect developmental dyslexia, which includes game-based techniques, reading and writing tests, facial image capture and analysis, eye tracking, Magnetic reasoning imaging (MRI) and Electroencephalography (EEG) scans. This survey paper critically analyzes recent contributions in detecting dyslexia using machine learning techniques and identify potential opportunities for future research. (C) 2020 The Korean Institute of Communications and Information Sciences (KICS). Publishing services by Elsevier B.V.
引用
收藏
页码:181 / 184
页数:4
相关论文
共 20 条
[1]  
[Anonymous], 2020, Dyslexia
[2]  
[Anonymous], 2020, DYSLEXIA IN AUSTRALI
[3]  
Asvestopoulou T., 2019, DYSLEXML SCREENING T
[4]   Screening for Dyslexia Using Eye Tracking during Reading [J].
Benfatto, Mattias Nilsson ;
Seimyr, Gustaf Oqvist ;
Ygge, Jan ;
Pansell, Tony ;
Rydberg, Agneta ;
Jacobson, Christer .
PLOS ONE, 2016, 11 (12)
[5]   Disrupted white matter connectivity underlying developmental dyslexia: A machine learning approach [J].
Cui, Zaixu ;
Xia, Zhichao ;
Su, Mengmeng ;
Shu, Hua ;
Gong, Gaolang .
HUMAN BRAIN MAPPING, 2016, 37 (04) :1443-1458
[6]  
European Dyslexia Association, 2020, What is dyslexia?
[7]  
Frid A., 2018, ARXIV PREPRINT ARXIV
[8]   Dyslexia Adaptive Learning Model: Student Engagement Prediction Using Machine Learning Approach [J].
Hamid, Siti Suhaila Abdul ;
Admodisastro, Novia ;
Manshor, Noridayu ;
Kamaruddin, Azrina ;
Abd Ghani, Abdul Azim .
RECENT ADVANCES ON SOFT COMPUTING AND DATA MINING (SCDM 2018), 2018, 700 :372-384
[9]  
Hamid SSA, 2015, 2015 9TH MALAYSIAN SOFTWARE ENGINEERING CONFERENCE (MYSEC2015), P284, DOI 10.1109/MySEC.2015.7475234
[10]  
Khan R.U., 2018, International Journal of Engineering & Technology, V7, P97