使用虛擬教室遊戲的基於融合的深度學習注意缺陷多動障礙評估方法

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姓名

黃程意(Cheng-Yi Huang) 查詢紙本館藏

畢業系所

軟體工程研究所

論文名稱

使用虛擬教室遊戲的基於融合的深度學習注意缺陷多動障礙評估方法
(Fusion-Based Deep Learning Assessment Method For Attention Deficit Hyperactivity Disorder Using Virtual Classroom Game)

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★ 基於V模型、醫療器材標準和FDA指南的新醫療器材軟體開發流程:以ADHD虛擬實境教室為例

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摘要(中)

注意缺陷多動障礙 (ADHD) 是一種神經發育障礙，通常發生在兒童
時期。ADHD 的主要症狀是難以集中注意力、過動、性格衝動，甚至
有破壞性的行為。 ADHD 通常在學齡前被診斷出來。如果不及時有
效地治療，症狀可能會持續到青春期，甚至到成年期。因此，通過
高效、有效和低成本的認知評估來檢測多動症患者非常重要。很多
時候，ADHD 的評估完全依賴於使用 BRS 進行患者行為觀察和評分的
醫生和家長，因此這些評估方法容易具有很強的主觀性。由於上述
缺點，我們更喜歡使用虛擬現實（VR）技術。 VR 可以通過多種傳
感器提供身臨其境的交互虛擬環境，讓我們更好地獲取用戶信息。
在這項研究中，我們設計了一個虛擬課堂遊戲。遊戲內容基於音頻
測試、CPT 和 Stroop 測試，加上我們在遊戲中設計的一些干擾事
件，以便我們盡量獲取我們需要的用戶的生理信息。生理信息主要
包括遊戲中的任務表現、腦電波、眼球運動軌跡、頭部旋轉幅度
等，分別轉化為多維數據集。然後通過統計分析確定這些數據集的
特徵在不同類型的用戶之間是否存在顯著差異。此外，隨著機器學
習和深度學習的飛速發展，我們也利用上述技術幫助我們構建了一
個能夠區分正常人和多動症患者的分類模型。由於我們有不同的數
據集，我們可以使用每個數據集來構建模型並評估模型的有效性。
甚至可以組合所有數據集來構建融合模型。最終結果表明，我們的
實驗具有很大的發展潛力。

摘要(英)

Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental
disorder that usually occurs in childhood. The main symptoms of ADHD
are inattention, hyperactivity, impulsivity, and even destructive behaviors.
ADHD is usually diagnosed before school age. If it is not treated
effectively and timely, the problems will continue into adolescence and
even into adulthood. Therefore, it is very important to screen people with
ADHD through efficient, effective and low-cost cognitive assessment. In
many times, the assessment of ADHD completely relies on doctors and
parents who use BRS for patient behavior observation and rating, so these
assessment methods prone to have a strong subjectivity. Due to the above
shortcomings, we prefer to use Virtual Reality (VR) technology. VR can
provide an immersive and interactive virtual environment with a variety
of sensors so that we can better obtain user information. In this research,
we designed a virtual classroom game. The content of the game is based
on audio test, CPT and Stroop test with some interference events we
designed in the game, so that we can try our best to get the physiological
information of the user we need. The physiological information mainly
includes the task performance in the game, brain waves, eye movement
trajectory, and head rotation amplitude, which are respectively converted
into a multi-dimensional dataset. Then determine whether the features of
these datasets are significantly different among different types of users
through statistical analysis. In addition, with the rapid development of
machine learning and deep learning, we have also used the above
technologies to help us build a classification model that can distinguish
between normal people and ADHD patients. Since we have diverse
datasets, we can use each dataset to build a model and evaluate the
effectiveness of the model. It is even possible to combine all the datasets
to build a fusion model. The final results show that our experiment has
great potential for development.

關鍵字(中)

★ 注意力不足過動症
★ 評估
★ 虛擬實境
★ 機器學習
★ 深度學習

關鍵字(英)

★ attention deficit hyperactivity disorder
★ assessment
★ virtual reality
★ machine learning
★ deep learning

論文目次

摘要……………………………………………………………………….I
Abstract………………………………………………………………….III
致謝……………………………………………………………………...V
Table of Contents………………………………………………………..VI
List of Figures…………………………………………………………VIII
List of Tables……………………………………………………………IX
1. Introduction……………………………………………………………1
2. Related Work…………………………………………………………..4
3. Methodology…………………………………………………………...7
3.1. System Architecture……………………………………………..7
3.2. VR Game Scenario…………………………………………….13
3.3. Experiment……………………………………………………..16
3.3.1. Subject…………………………………………………..16
3.3.2. Procedure………………………………………………..16
3.4. Analysis Method……………………………………………….17
3.4.1. Feature Extraction……………………………………….17
3.4.2. Statistical Analysis………………………………………18
3.4.3. Machine Learning → Deep Learning…………………...19
3.4.4. Fusion Model……………………………………………19
4. Discussion…………………………………………………………….25
5. Result…………………………………………………………………27
6. Conclusion & Future Work…………………………………………...31
Reference………………………………………………………………..32

參考文獻

[1] Sandra JJ Kooij, Susanne Bejerot, et al. “European consensus statementon diagnosis and treatment of adult
ADHD: The European Network AdultADHD.” BMC psychiatry (2010).
[2] Pliszka, Steven R. ”Patterns of psychiatric comorbidity with attentiondeficit/hyperactivity
disorder.” Child and adolescent psychiatric clinics ofNorth America (2000).
[3] Stephen V. Faraone, Joseph Biederman and Eric Mick. “The age-dependent
decline of attention deficit hyperactivity disorder: a meta-analysis of follow-up studies.”
Psychological Medicine (2006): 159-165.
[4] Parsons T D, Bowerly T, Buckwalter J G, et al. “A controlled clinicalcomparison of
attention performance in children with ADHD in a virtualreality classroom compared to standard
neuropsychological methods.”Child Neuropsychology (2007): 363-381.
[5] A. Negut,, S.-A. Matu, F. A. Sava, and D. David, “Virtual reality measuresin neuropsychological assessment:
a meta-analytic review,” The ClinicalNeuropsychologist, vol. 30, no. 2, pp. 165-184, 2016/02/17, 2016.
[6] A. Moreno, K. J. Wall, K. Thangavelu, L. Craven, E. Ward, and N.N. Dissanayaka,
“A systematic review of the use of virtual reality andits effects on cognition in
individuals with neurocognitive disorders,”Alzheimer’s & dementia (New York, N. Y.), vol. 5, pp. 834-850,
2019.
[7] Chin-Teng Lin, I-Fang Chung, Li-Wei K, Yu-Chieh Chen, Sheng-FuLiang, Jeng-Ren
Duann. “EEG-Based Assessment of Driver CognitiveResponses in a Dynamic Virtual-Reality
Driving Environment.” IEEETransactions on Biomedical Engineering. July 2007; 54: 1349-1352
[8] Shih-Ching Yeh, Yuan-Yuan Li, Chu Zhou, Pin-Hua Chiu, Jun-Wei Chen.“Effects of Virtual Reality and
Augmented Reality on Induced Anxiety.”IEEE Transactions on Neural Systems and Rehabilitation
Engineering.July 2018; 26: 1345-1352
[9] Khalid A. Alahmari, Patrick J. Sparto, Gregory F. Marchetti, Mark S.Redfern, Joseph
M. Furman, and Susan L. Whitney. “Comparison ofVirtual Reality Based Therapy With
Customized Vestibular PhysicalTherapy for the Treatment of Vestibular Disorders” IEEE
Transactionson Neural Systems and Rehabilitation Engineering. March 2014; 22:389-399
[10] Ilaria Bortone, Daniele Leonardis, Nicola Mastronicola, AlessandraCrecchi, et al.
“Wearable Haptics and Immersive Virtual Reality Re-habilitation Training in Children With
Neuromotor Impairments.” IEEETransactions on Neural Systems and Rehabilitation Engineering.
July2018; 26:1469-1478
[11] Shih-Ching Yeh, Sheng-Yang Lin, Eric Hsiao-Kuang Wu, Kai-FengZhang, Xiu Xu, Albert
Rizzo, Chia-Ru Chung, “A Virtual-Reality SystemIntegrated with Neuro-Behavior Sensing for AttentionDeficit / Hyper-activity Disorder Intelligent Assessment”, IEEE Transaction on NeuralSystems
and Rehabilitation,2020
[12] Gagliano, Marcelo Pham, John Tang, Boyang Kashif, Hiba Ban,James. (2017).
Applications of Machine Learning in Medical Diagnosis.
[13] Zygouris S, Giakoumis D, Votis K, Doumpoulakis S, Ntovas K, SegkouliS, Karagiannidis C, Tzovaras D,
Tsolaki M. Can a virtual reality cognitivetraining application fulfill a dual role? Using the virtual
supermarketcognitive training application as a screening tool for mild cognitiveimpairment. J
Alzheimers Dis. 2015;44(4):1333-47. doi: 10.3233/JAD-141260. PMID: 25428251.
[14] Werner P, Rabinowitz S, Klinger E, Korczyn AD, Josman N. Useof the virtual
action planning supermarket for the diagnosis of mildcognitive impairment: a preliminary study.
Dement Geriatr Cogn Disord.2009;27(4):301-9. doi: 10.1159/000204915. Epub 2009 Mar 2.
PMID:19252401.
[15] Zygouris S, Ntovas K, Giakoumis D, Votis K, Doumpoulakis S, SegkouliS, Karagiannidis C, Tzovaras
D, Tsolaki M. A Preliminary Study onthe Feasibility of Using a Virtual Reality Cognitive Training
Applicationfor Remote Detection of Mild Cognitive Impairment. J Alzheimers Dis.2017;56(2):619-627. doi:
10.3233/JAD-160518. PMID: 28035922.
[16] Nishi, T., Fukudome, K., Hata, K. et al. Effectiveness of simple tracingtest as an objective evaluation of
hand dexterity. Sci Rep 9, 9915 (2019).https://doi.org/10.1038/s41598-019-46356-9
[17] D. Impedovo and G. Pirlo, ”Dynamic Handwriting Analysis for theAssessment of
Neurodegenerative Diseases: A Pattern Recognition Per-spective,” in IEEE Reviews in Biomedical
Engineering, vol. 12, pp. 209-220, 2019, doi: 10.1109/RBME.2018.2840679.
[18] D. Yang and K. Hong, ”Comparison of Neural Biomarker AssessmentMethods for
Early Detection of Alzheimer’s Disease,” 2019 Interna-tional Conference on Mechatronics,
Robotics and Systems Engineering(MoRSE), 2019, pp. 34-39, doi: 10.1109/MoRSE48060.2019.8998674.
[19] A. S. Panayides et al., ”AI in Medical Imaging Informatics: CurrentChallenges and
Future Directions,” in IEEE Journal of Biomedical andHealth Informatics, vol. 24, no. 7,
pp. 1837-1857, July 2020, doi:10.1109/JBHI.2020.2991043.
[20] Celestino Rodr ́ıguez et.al “Comparison between two continuous per-formance tests for
identifying ADHD: Traditional vs. virtual Reality.”International Journal of Clinical and Health
Psychology.
[21] Areces D, Dockrell J, Garc ̃o a T, Gonza lez-Castro P, Rodr ̃o guez C(2018)
“Analysis of cognitive and attentional profiles in children withand without ADHD using an
innovative virtual reality tool.” PLoSONE13(8): e0201039. https://doi.org/10.1371/journal.pone.0201039
[22] Vahid A, Bluschke A, Roessner V, Stober S, Beste C. Deep LearningBased on
Event-Related EEG Differentiates Children with ADHD fromHealthy Controls. J Clin Med.
2019;8(7):1055. Published 2019 Jul 19.
[23] Zulueta, Aitziber Diaz-Orueta, Unai Crespo, Nerea Torrano, Fermin.(2018). Virtual Realitybased Assessment and Rating Scales in ADHDDiagnosis. Psicolog ́ıa Educativa.
[24] J. Shi, X. Zheng, Y. Li, Q. Zhang and S. Ying, ”Multimodal Neu-roimaging Feature
Learning With Multimodal Stacked Deep PolynomialNetworks for Diagnosis of Alzheimer’s Disease,”
in IEEE Journal ofBiomedical and Health Informatics, vol. 22, no. 1, pp. 173-183,
Jan.2018, doi: 10.1109/JBHI.2017.2655720.
[25] F. J. Martinez-Murcia, A. Ortiz, J. -M. Gorriz, J. Ramirez and D.CastilloBarnes, ”Studying the Manifold Structure of Alzheimer’s Dis-ease: A Deep Learning
Approach Using Convolutional Autoencoders,”in IEEE Journal of Biomedical and Health
Informatics, vol. 24, no. 1,pp. 17-26, Jan. 2020, doi: 10.1109/JBHI.2019.2914970.
[26] P. Telagarapu, B. Mohanty and K. R. Anandh, ”Analysis ofAlzheimer
Condition in T1-Weighted MR Images Using Texture Fea-tures and K-NN Classifier,” 2018
International CET Conference onControl, Communication, and Computing (IC4), 2018, pp. 331-334,
doi:10.1109/CETIC4.2018.8530943.
[27] W. -L. Zheng, W. Liu, Y. Lu, B. -L. Lu and A. Cichocki, ”EmotionMeter:A Multimodal Framework for
Recognizing Human Emotions,” in IEEETransactions on Cybernetics, vol. 49, no. 3, pp. 1110-1122, March
2019,doi: 10.1109/TCYB.2018.2797176.
[28] X. Zhang et al., ”Emotion Recognition From Multimodal PhysiologicalSignals Using a Regularized Deep F
[29] HTC Vive Helmet and Controller, from HTC Vive accessed 1 July 2021, https://www.vive.com/tw/
[30] Looxid Link Device and User Interface, from Looxid Link accessed 1 July 2021,
https://looxidlink.looxidlabs.com/
[31] Looxid Link User Situation, from Looxid Link accessed 1 July 2021,
https://www.facebook.com/looxidlabs/

指導教授

葉士青吳曉光(Shih-Ching Yeh Xiang-Guang Wu)

審核日期

2021-8-24

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