基於VR的自閉症兒童多模態訓練系統的改進

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姓名

舒坦(SHU,TAN) 查詢紙本館藏

畢業系所

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論文名稱

基於VR的自閉症兒童多模態訓練系統的改進
(The Improvement of a VR-based Multimodal Training System for Children with Autism Spectrum Disorder)

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摘要(中)

自閉症譜系障礙 (ASD) 的特點是社交溝通、社交互動和重複行為困難。然而，診斷和預測兒童的症狀可能具有挑戰性，並且較輕的症狀可能會被忽視，直到他們達到學齡時社會活動增加。此外，為 ASD 提供有效治療需要考慮勞動力成本和分配額外資源。 COVID-19 大流行擾亂了 ASD 患者的早期治療和團體治療。儘管如此，虛擬現實 (VR) 技術提供了一種解決方案，它提供模擬的人際互動場景並為患有 ASD 的兒童提供正常訓練。本研究將虛擬現實技術與可穿戴多模式傳感技術設備相結合，記錄遊戲訓練過程中的生理信號和遊戲表現數據。與傳統培訓方法相比，這種方法不僅提高了用戶參與度，而且還提供了一種更有效的評估和治療 ASD 的方法。 VR 的身臨其境和互動特性允許現實可控的社交情境，使自閉症患者能夠在安全和支持的環境中練習和發展社交技能。最終，這種創新方法有可能徹底改變 ASD 的評估和治療，為個人提供更好的社會和行為發展。

摘要(英)

Autism Spectrum Disorders (ASD) are characterized by difficulties in social communication, social interaction, and repetitive behaviors. However, diagnosing and predicting symptoms in children can be challenging, and milder symptoms may go unnoticed until they reach school age when social activities increase. Additionally, providing effective treatment for ASD requires considering labor costs and allocating additional resources. The COVID-19 pandemic has disrupted early treatment and group therapy for individuals with ASD. Nevertheless, virtual reality (VR) technology offers a solution by providing simulated human interaction scenarios and enabling normal training for children with ASD. This research combines VR technology with wearable multimodal sensing technology devices together recording physiological signals and game performance data during game training. This approach not only enhances user participation but also provides a more efficient way to assess and treat ASD compared to traditional training methods. The immersive and interactive nature of VR allows for realistic and controlled social situations, enabling individuals with ASD to practice and develop social skills in a safe and supportive environment. Ultimately, this innovative approach has the potential to revolutionize the assessment and treatment of ASD, providing individuals with better opportunities for social and behavioral development.

關鍵字(中)

★ 自閉症譜系障礙
★ 虛擬現實
★ 腦電
★ 眼球追蹤

關鍵字(英)

★ Autism Spectrum Disorder(ASD)
★ Virtual Reality(VR)
★ EEG
★ ET

論文目次

Table of Content 摘要 ......................... V
Abstract ................................. VI
致謝 ............................................... VII
List of Figures ....................................... IX
List of Tables .................................... XI
Introduction ..................................... 1
Related Works .......................... 12
Methodology................................. 15
Result ................................................ 28
Conclusion ......................................... 38
Reference................................ 39

參考文獻

[1] Kliegman, R., et al., Nelson textbook of pediatrics. 2020.
[2] Shevell, M., Swaimans pediatric neurology - principles and practice. 2017.
[3] Diagnostic and Statistical Manual of Mental Disorders (DSM–5). Available from:
https://www.psychiatry.org/psychiatrists/practice/dsm.
[4] P. Parijat, T. E. Lockhart, and J. Liu, “EMG and kinematic responses to unexpected slips after slip training
in virtual reality,” IEEE Trans. Biomed. Eng., vol. 62, no. 2, pp. 593–599, Feb. 2015.
[5] R. R. Kaliki, R. Davoodi, and G. E. Loeb, “Evaluation of a noninvasive command scheme for upper-limb
prostheses in a virtual rea lity reach and grasp task,” IEEE Trans. Biomed. Eng., vol. 60, no. 3, pp. 792–802,
Mar.2013.
[6] L. E. Sucar, F. Orihuela -Espina, R. L. Velazquez, D. J. Reinkensmeyer, R. Leder, and J. Hernandez-Franco,
“Gesture therapy: An upper limb virtual reality-based motor rehabilitation platform,” IEEE Trans. Neural
Syst. Rehabil. Eng., vol. 22, no. 3, pp. 634–643, May 2014
[7] M. Gagliano, J. Pham, B. Tang, H. Kashif, and J. Ban, Applications of Machine Learning in Medical
Diagnosis. 2017.Available from: https://www.researchgate.net/publication/321151498 Applications of
Machine Learning in Medical Diagnosis
[8] Jones, W., K. Carr, and A. Klin, Absence of preferential looking to the eyes of approaching adults predicts
level of social disability in 2yearold toddlers with autism spectrum disorder. Arch Gen Psychiatry, 2008.
65(8): p. 946-54.
[9] Klin, A., et al., Visual fixation patterns during viewing of naturalistic social situations as predictors of
social competence in individuals with autism. Arch Gen Psychiatry, 2002. 59(9): p. 809-16.
[10] Babu, P.R.K., P. Oza, and U. Lahiri, Gaze-Sensitive Virtual Reality Based Social Communication Platform
for Individuals with Autism. IEEE Transactions on Affective Computing, 2018. 9(4): p. 450 -462.
[11] W. Bosl, A. Tierney, H. Tager-Flusberg, and C. Nelson, “EEG complexity as a biomarker for autism
spectrum disorder risk,” BMC Med., vol. 9, no. 1, p. 18, Feb. 2011. [Online]. Available:
http://bmcmedicine.biomedcentral.com/articles/10.1186/1741-7015-9-18
[12] H. Hadoush, M. Alafeef, and E. Abdulhay, “Brain complexity in children with mild and severe autism
spectrum disorders: Analysis of multiscale entropy in EEG,” Brain Topography, vol. 32, no. 5, pp. 914 –921,
Sep. 2019. [Online]. Available: https://pubmed.ncbi.nlm.nih.gov/31006838/
[13] J. M. Peters et al., “Bra in functional networks in syndromic and nonsyndromic autism: A graph theoretical
study of EEG connectivity,” BMC Med., vol. 11, no. 1, p. 54, Feb. 2013. [Online]. Available:
https://bmcmedicine.biomedcentral.com/articles/10.1186/1741-701511-54
[14] K. Zeng et al., “Disrupted brain network in children with autism spectrum disorder,” Sci. Rep., vol. 7, no. 1,
Dec. 2017, Art. no. 16253. [Online]. Available: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5701151/
[15] W. J. Bosl, H. Tager-Flusberg, and C. A. Nelson, “EEG a nalytics for early detection of autism spectrum
32
disorder: A data -driven approach,” Sci. Rep., vol. 8, no. 1, p. 6828, Dec. 2018. [Online]. Available:
https://pubmed.ncbi.nlm.nih.gov/29717196/
[16] F. H. Duffy and H. Als, “Autism, spectrum or clusters? An EEG coherence study,” BMC Neurol., vol. 19,
no. 1, p. 27, Feb. 2019. [Online]. Available: https://bmcneurol.biomedcentral.com/articles/10.1186/s12883 -
0191254-1
[17] Y.-T. Fan, J. Decety, C.-Y. Yang, J.-L. Liu, and Y. Cheng, “Unbroken mirror neurons in autism spectrum
disorders,” J. Child Psychol. Psychiat., vol. 51, no. 9, pp. 981–988, Sep. 2010. [Online]. Available:
http://doi.wiley.com/10.1111/j.1469-7610.2010.02269.x
[18] M. Jaime, C. M. McMahon, B. C. Davidson, L. C. Newell, P. C. Mundy, and H. A. Henderson, “Brief
report: Reduced temporalcentral eeg alpha coherence during joint attention perception in adolescents with
autism spectrum disorder,” J. Autism Develop. Disord., vol. 46, no. 4, pp. 1477–1489, Apr. 2016. [Online].
Available: https://link.springer.com/article/10.1007/s10803-015-2667-3
[19] L. Billeci et al., “An integrated EEG and eye-tracking approach for the study of responding and initiating
joint attention in autism spectrum disorders,” Sci. Rep., vol. 7, no. 1, pp. 1 –13, Dec. 2017. [Online].
Available: http://www.michelangelo-project.eu/
[20] C. Hollis, C. J. Falconer, J. L. Martin, C. Whittington, S. Stockton, C. Glazebrook, and E. B. Davies,
“Annual research review: Digital health interventions for children and young people with mental health
problems—A systematic and meta—Review,” J. Child Psychol. Psychiatry, vol. 58, no. 4, pp. 474–503,
Apr. 2017.
[21] D. C. Mohr, S. M. Schueller, E. Montague, M. N. Burns, and Rashidi, “The behavioral intervention
technology model: An integrated conceptual and technological framework for eHealth and mHealth
interventions,” J. Med. Internet Res., vol. 16, no. 6, p. e146, Jun. 2014.
[22] U. Lee, K. M. Han, H. Cho, K. Chung, H. Hong, S. J. Lee, Y. Noh, S. Park, and J. M. Carroll, “ Intelligent
positive computing with mobile, wearable, and IoT devices: Literature review and research directions,”

指導教授

葉士青(Shih-Ching Yeh)

審核日期

2023-7-26

推文