基於眼動的閱讀障礙分析與診斷

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：53

、訪客IP：18.117.183.172

姓名

楊軒宇(Hsuan-Yu Yang) 查詢紙本館藏

畢業系所

軟體工程研究所

論文名稱

基於眼動的閱讀障礙分析與診斷
(Analysis and Diagnosis of Dyslexia Based on Eye Movement)

相關論文

★ 探討Media-Pipe和Leap Motion藉由用於發展遲緩的VR系統在分層共現網路的對指手勢預測精準度比較	★ 在有干擾的虛擬教室環境下大人小孩的行為表現與腦神經反應的異同
★ 使用映射模型和跨資料集遷移式學習的輕量化居家衰弱症訓練系統	★ 心率生理回饋放鬆訓練對於海洛因使用疾患(HUD)生理資訊之影響分析
★ 基於深度學習模型的3D心理旋轉對認知障礙的診斷與評估	★ 評估注意力偵測之穿戴式腦電電極放置有效性

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

至系統瀏覽論文 ( 永不開放)

摘要(中)

閱讀障礙，是指在閱讀和寫作方面有困難，但沒有明顯的智力缺陷，也沒有
相關的視覺或聽覺障礙的病症。症狀的嚴重程度因文化和個人因素而異。其他可
能的症狀包括拼寫單詞困難，朗讀速度較慢，以及無法在頭腦中說出單詞。然而，
要完全診斷出閱讀障礙是很困難的，因為當事人必須首先證明學習成績低下，而
文化剝奪是導致成績低下的一種環境學習形式。
因此，有必要提供一種快速有效的診斷輔助和學習幫助。隨著虛擬現實、眼
動和機器學習的快速發展，我們建立了一個虛擬閱讀環境，並從收集到的眼動生
理信息中計算出三個特徵集，包括眼動特徵、句子和視覺顯著圖。我們還提出了
一個融合模型，整合了多個機器學習模型，通過評估相關數據來評估閱讀障礙，
並利用從用戶反應中獲得的生理數據，建立一個基於真實數據的更客觀的自動評
估模型。

摘要(英)

Dyslexia, or Reading Disorder, is a condition in which a person has difficulty reading and
writing without significant intellectual deficits and without associated visual or auditory
impairments. The severity of symptoms varies depending on cultural and personal factors.
Other possible symptoms include difficulty spelling words, slower reading aloud, and inability
to say words in the head. However, it is difficult to fully diagnose dyslexia because the person
must first demonstrate low academic achievement, and cultural deprivation is a form of
environmental learning that causes low achievement. Therefore, there is a need to provide a
fast and effective diagnostic aid and learning aid. With the rapid development of virtual reality,
eye-movement and machine learning, we build a virtual reading environment and compute
three feature sets, including eye-movement features, word vectors and saliency maps, from
the collected eye-movement physiological information. We also propose a fusion model that
integrates several machine learning models to assess dyslexia by evaluating relevant data, and
build a more objective automatic assessment model based on real data by using physiological
data obtained from users′ responses are able to provide a more effective system in
Methamphetamine treatment.

關鍵字(中)

★ 閱讀障礙
★ 眼球運動
★ 數據融合
★ 虛擬現實
★ 機器學習

關鍵字(英)

★ Dyslexia
★ Eye movement
★ Data fusion
★ Virtual Reality
★ Machine learning

論文目次

Table of Contents
摘要........................................................................................................................I
Abstract ................................................................................................................ II
致謝.....................................................................................................................III
Table of Contents.................................................................................................IV
List of Figures ...................................................................................................... V
List of Tables.......................................................................................................VI
1. Introduction................................................................................................... 1
2. Related Works............................................................................................... 7
3. Experiment Setup........................................................................................ 12
4. Results......................................................................................................... 23
5. Conclusion and Future Work ...................................................................... 33
Reference............................................................................................................. 35

參考文獻

[1] NINDS Dyslexia Information Page. National Institute of Neurological Disorders and Stroke. National Institutes of Health. 2015-09-
11 [2022-07-01].
[2] Siegel LS. Perspectives on dyslexia. Paediatrics & Child Health. November 2006, 11 (9): 581–7. PMC 2528651. PMID 19030329.
doi:10.1093/pch/11.9.581.
[3] Statistics Department, Ministry of Health and Welfare, "Number of Persons with Disabilities by Category, County and City",
https://dep.mohw.gov.tw/dos/cp-5224-62359-113.html[2022-07-09]
[4] Peterson, RL; Pennington, BF. Developmental dyslexia. (PDF). Lancet. 2012-05-26, 379 (9830): 1997–2007 [2015-06-13]. PMID
22513218. doi:10.1016/s0140-6736(12)60198-6
[5] Kooij, J. J. Sandra. Adult ADHD diagnostic assessment and treatment 3rd. London: Springer. 2013: 83 [2018-04-07]. ISBN
9781447141389.
[6] What are common treatments for reading disorders?. National Institutes of Health. [2022-07-05]
[7] How are reading disorders diagnosed?. National Institutes of Health.
[8] Instructions for the identification of students with learning
disabilities.http://teacher.mhups.tp.edu.tw/eweb/module/download/update/ew00000000104/file4840_14.pdf [2022-07-09]
[9] Kunert R, Scheepers C. Speed and accuracy of dyslexic versus typical word recognition: an eye-movement investigation. Front
Psychol. 2014 Oct 9;5:1129. doi: 10.3389/fpsyg.2014.01129. PMID: 25346708; PMCID: PMC4191135.
[10] Jarrad A.G. Lum, Michael T. Ullman, Gina Conti-Ramsden,Procedural learning is impaired in dyslexia: Evidence from a metaanalysis of serial reaction time studies,Research in Developmental Disabilities,Volume 34, Issue 10,2013,Pages 3460-3476,ISSN
0891-4222
[11] Chung, Kevin & McBride-Chang, Catherine & Wong, Simpson & Cheung, Him & Penney, Trevor & Ho, Connie. (2008). The role of
visual and auditory temporal processing for Chinese children with developmental dyslexia. Annals of dyslexia. 58. 15-35.
10.1007/s11881-008-0015-4.
[12] Chung KKH, Lam CB. Cognitive-Linguistic Skills Underlying Word Reading and Spelling Difficulties in Chinese Adolescents With
Dyslexia. Journal of Learning Disabilities. 2020;53(1):48-59. doi:10.1177/0022219419882648
[13] Chung, K.K.H., Lo, J.C.M. & McBride, C. Cognitive-linguistic profiles of Chinese typical-functioning adolescent dyslexics and
high-functioning dyslexics. Ann. of Dyslexia 68, 229–250 (2018). https://doi.org/10.1007/s11881-018-0165-y
[14] H. Ranjbartabar, D. Richards, A. A. Bilgin and C. Kutay, "First Impressions Count! The Role of the Human′s Emotional State on
Rapport Established with an Empathic versus Neutral Virtual Therapist," in IEEE Transactions on Affective Computing, vol. 12, no.
3, pp. 788-800, 1 July-Sept. 2021, doi: 10.1109/TAFFC.2019.2899305.
[15] L. Bozgeyikli, A. Raij, S. Katkoori and R. Alqasemi, "A Survey on Virtual Reality for Individuals with Autism Spectrum Disorder:
Design Considerations," in IEEE Transactions on Learning Technologies, vol. 11, no. 2, pp. 133-151, 1 April-June 2018, doi:
10.1109/TLT.2017.2739747.
[16] C. Meng and X. Zhao, "Webcam-Based Eye Movement Analysis Using CNN," in IEEE Access, vol. 5, pp. 19581-19587, 2017, doi:
10.1109/ACCESS.2017.2754299.
[17] Y. Mao, Y. He, L. Liu and X. Chen, "Disease Classification Based on Synthesis of Multiple Long Short-Term Memory ClassifiersCorresponding to Eye Movement Features," in IEEE Access, vol. 8, pp. 151624-151633, 2020, doi:
10.1109/ACCESS.2020.3017680.
[18] Georgina Rippon, Nicola Brunswick, Trait and state EEG indices of information processing in developmental dyslexia, International
Journal of Psychophysiology, Volume 36, Issue 3,2000,Pages 251-265,ISSN 0167-8760
[19] Perera H, Shiratuddin MF, Wong KW. Review of EEG-based pattern classification frameworks for dyslexia. Brain Inform. 2018 Jun
15;5(2):4. doi: 10.1186/s40708-018-0079-9. PMID: 29904812; PMCID: PMC6094381.
[20] O. L. Usman, R. C. Muniyandi, K. Omar and M. Mohamad, "Advance Machine Learning Methods for Dyslexia Biomarker
Detection: A Review of Implementation Details and Challenges," in IEEE Access, vol. 9, pp. 36879-36897, 2021, doi:
10.1109/ACCESS.2021.3062709.
[21] Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N. and Kaiser,
Lukasz and Polosukhin, Illia. Attention Is All You Need. arXiv. 2017.
[22] M. Ahmadi, M. Hajabdollahi, N. Karimi and S. Samavi, "Context-Aware Saliency Map Generation Using Semantic Segmentation,"
Electrical Engineering (ICEE), Iranian Conference on, 2018, pp. 616-620, doi: 10.1109/ICEE.2018.8472577.
[23] Y. Lin, H. Ma, Z. Pan and R. Wang, "Depression Detection by Combining Eye Movement with Image Semantics," 2021 IEEE
International Conference on Image Processing (ICIP), 2021, pp. 269-273, doi: 10.1109/ICIP42928.2021.9506702.
[24] Shivani Choudhary, Kushagri Tandon, Raksha Agarwal, and Niladri Chatterjee. 2021. MTL782_IITD at CMCL 2021 Shared Task:
Prediction of Eye-Tracking Features Using BERT Embeddings and Linguistic Features. In Proceedings of the Workshop on
Cognitive Modeling and Computational Linguistics, pages 114–119, Online. Association for Computational Linguistics.
[25] A. Elnakib, A. Soliman, M. Nitzken, M. F. Casanova, G. Gimel’farb and A. El-Baz, "Magnetic resonance imaging findings for
dyslexia: A review", J. Biomed. Nanotechnol., vol. 10, no. 10, pp. 2778-2805, Oct. 2014.
[26] J. M. Fletcher et al., "Classification of learning disabilities: An evidence-based evaluation" in Identification of Learning Disabilities:
Research to Practice, Washington, DC, USA:Erlbaum Associates Publishers, pp. 185-250, Jan. 2002.
[27] F. Ramus, I. Altarelli, K. Jednoróg, J. Zhao and L. Scotto di Covella, "Neuroanatomy of developmental dyslexia: Pitfalls and
promise", Neurosci. Biobehav. Rev., vol. 84, pp. 434-452, Jan. 2018.
[28] U. Kuhl, N. E. Neef, I. Kraft, G. Schaadt, L. Dörr, J. Brauer, et al., "The emergence of dyslexia in the developing brain",
NeuroImage, vol. 211, May 2020.
[29] N. A. M. Yuzaidey, N. C. Din, M. Ahmad, N. Ibrahim, R. A. Razak and D. Harun, "Interventions for children with dyslexia: A review
on current intervention methods", Med. J. Malaysia, vol. 73, no. 5, pp. 311-320, Oct. 2018.
[30] L. Danelli, M. Berlingeri, G. Bottini, F. Ferri, L. Vacchi, M. Sberna, et al., "Neural intersections of the phonological visual
magnocellular and motor/cerebellar systems in normal readers: Implications for imaging studies on dyslexia", Hum. Brain Mapping,
vol. 34, no. 10, pp. 2669-2687, Oct. 2013.
[31] F. Wadehn, T. Weber, D. J. Mack, T. Heldt and H. -A. Loeliger, "Model-Based Separation, Detection, and Classification of Eye
Movements," in IEEE Transactions on Biomedical Engineering, vol. 67, no. 2, pp. 588-600, Feb. 2020, doi:
10.1109/TBME.2019.2918986.
[32] Y. Mao, Y. He, L. Liu and X. Chen, "Disease Classification Based on Synthesis of Multiple Long Short-Term Memory Classifiers
Corresponding to Eye Movement Features," in IEEE Access, vol. 8, pp. 151624-151633, 2020, doi:
10.1109/ACCESS.2020.3017680.
[33] A Jothi Prabha, R Bhargavi,Predictive Model for Dyslexia from Fixations and Saccadic Eye Movement Events,Computer Methods
and Programs in Biomedicine,Volume 195,2020,105538,ISSN 0169-2607.
[34] Maria De Luca, Enrico Di Pace, Anna Judica, Donatella Spinelli, Pierluigi Zoccolotti, Eye movement patterns in linguistic and nonlinguistic tasks in developmental surface dyslexia,Neuropsychologia,Volume 37, Issue 12,1999,Pages 1407-1420,ISSN 0028-3932.
[35] Kerstin I. Schattka, Ralph Radach, Walter Huber, Eye movement correlates of acquired central dyslexia, Neuropsychologia, Volume
48, Issue 10, 2010, Pages 2959-2973, ISSN 0028-3932.
[36] Peter Raatikainen, Jarkko Hautala, Otto Loberg, Tommi Kärkkäinen, Paavo Leppänen, Paavo Nieminen, Detection of developmental
dyslexia with machine learning using eye movement data Array, Volume 12, 2021, 100087, ISSN 2590-0056,
[37] Y. García Chimeno, B. García Zapirain, I. Saralegui Prieto and B. Fernandez-Ruanova, "Automatic classification of dyslexic children
by applying machine learning to fMRI images", Bio-Med. Mater. Eng., vol. 24, no. 6, pp. 2995-3002, 2014.
[38] A. Frid and Z. Breznitz, "An SVM based algorithm for analysis and discrimination of dyslexic readers from regular readers using
ERPs", Proc. IEEE 27th Conv. Elect. Electron. Eng. Israel, pp. 1-4, Nov. 2012.
[39] C. I. Eke, A. A. Norman and L. Shuib, "Context-Based Feature Technique for Sarcasm Identification in Benchmark Datasets Using
Deep Learning and BERT Model," in IEEE Access, vol. 9, pp. 48501-48518, 2021, doi: 10.1109/ACCESS.2021.3068323.
[40] R. Cai et al., "Sentiment Analysis About Investors and Consumers in Energy Market Based on BERT-BiLSTM," in IEEE Access, vol.
8, pp. 171408-171415, 2020, doi: 10.1109/ACCESS.2020.3024750.
[41] J. He and H. Hu, "MF-BERT: Multimodal Fusion in Pre-Trained BERT for Sentiment Analysis," in IEEE Signal Processing Letters,
vol. 29, pp. 454-458, 2022, doi: 10.1109/LSP.2021.3139856.
[42] S. Lee, D. K. Han and H. Ko, "Multimodal Emotion Recognition Fusion Analysis Adapting BERT With Heterogeneous Feature
Unification," in IEEE Access, vol. 9, pp. 94557-94572, 2021, doi: 10.1109/ACCESS.2021.3092735.
[43] Bender, W. N . (2004). Learning disabilities: Characteristics ,identification , and teaching strategies . Boston: Allyn and Bacon.

指導教授

葉士青吳曉光(Shih-Ching Yeh Eric Hsiao-kuang Wu)

審核日期

2022-8-22

推文