博碩士論文 109522061 詳細資訊




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姓名 張采庭(Tsai-Ting Chang)  查詢紙本館藏   畢業系所 資訊工程學系
論文名稱 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
(A Deep Learning-based Diagnosis and Assessment of Cognitive Impairment with 3D Mental Rotation)
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摘要(中) 輕度認知障礙 (MCI) 和阿爾茨海默病 (AD) 都是對全球具有嚴重影響的認知疾病。有研究指出,如果這些症狀能夠及早被發現,將可以減緩疾病的惡化,甚至痊癒。目前常見的認知評估和診斷方法大多依賴人為操作,而且結果常常被質疑是否過於主觀,因此一種方便、客觀和準確率高的新型診斷方法是目前迫切需要的。隨著資訊爆炸時代的到來,虛擬現實(VR)和人工智慧(AI)的技術受到了高度的關注。本研究提出了一種智慧認知評估和診斷方法,利用機器學習(ML)和深度學習(DL)結合VR技術,打造了一款3D心理旋轉測試模組。與過去傳統的紙本和2D心理旋轉測試相比,它收集了使用者在解題時的數據,並且可以通過操作手把玩成模組任務,藉此來進行空間認知訓練,同時會記錄答題過程中的手把操作狀態。通過這些原始數據和設計的192個特徵,再使用AI 技術構建分類器,能夠將MCI或AD患者與健康人區分開來,最後完成一個準確率約90%的認知功能智慧診斷模型,這也證明了空間訓練在認知評估中的可行性。
摘要(英) Mild Cognitive Impairment (MCI) and Alzheimer′s disease (AD) are both cognitive diseases with enormous global impact. The studies have shown that if these symptoms are detected and acted on early, disease progression can be slowed or even cured. The current common cognitive assessment and screening methods rely more on manual operations, and the results are often questioned as being overly subjective. As a result, a convenient, objective and highly accurate diagnostic method is necessary. With the advent of the era of information explosion, the technologies of virtual reality (VR) and artificial intelligence (AI) have received leaping attention. This study proposes an intelligent cognitive assessment and diagnosis method. Utilizing machine learning and deep learning combined with VR technology to create a 3D mental rotation test module. Compared with the traditional paper and 2D mental rotation tests in the past, it has collected richer user-solving data. Users can conduct spatial cognition training on this module by manipulating the handle, and at the same time record the operation status in the process. Through these raw data and the 192 designed features, AI technology was integrated to construct a classifier to distinguish MCI or AD patients from healthy people. Finally, an intelligent diagnostic model of cognitive function with an accuracy rate of nearly 90% was integrated, which also proved the feasibility of spatial cognition in cognitive assessment.
關鍵字(中) ★ 心理旋轉(MR)
★ 認知障礙
★ 虛擬現實(VR)
★ 機器學習(ML)
★ 深度學習(DL)
關鍵字(英) ★ Mental Rotation (MR)
★ Cognitive Impairment
★ Virtual reality (VR)
★ Machine Learning (ML)
★ Deep Learning (DL)
論文目次 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. Method 12
4. Results 23
5. Discussions 31
6. Conclusion and Future Works 33
Reference 35
參考文獻 [1] W. H. Organization. "Dementia," Jun. 14, 2020; https://www.who.int/news-room/fact-sheets/detail/dementia.
[2] Livingston, G., Huntley, J., Sommerlad, A., Ames, D., Ballard, C., Banerjee, S., ... & Mukadam, N. (2020). Dementia prevention, intervention, and care: 2020 report of the Lancet Commission. The Lancet, 396(10248), 413-446.
[3] Rechel, B., Grundy, E., Robine, J. M., Cylus, J., Mackenbach, J. P., Knai, C., & McKee, M. (2013). Ageing in the European union. The Lancet, 381(9874), 1312-1322.
[4] Prince, M., Bryce, R., Albanese, E., Wimo, A., Ribeiro, W., & Ferri, C. P. (2013). The global prevalence of dementia: a systematic review and metaanalysis. Alzheimer′s & dementia, 9(1), 63-75.
[5] Alzheimer′s Association. (2016). 2016 Alzheimer′s disease facts and figures. Alzheimer′s & Dementia, 12(4), 459-509.
[6] Yiannopoulou, K. G., & Papageorgiou, S. G. (2020). Current and future treatments in Alzheimer disease: an update. Journal of central nervous system disease, 12, 1179573520907397.
[7] Vikbladh, O. M., Meager, M. R., King, J., Blackmon, K., Devinsky, O., Shohamy, D., ... & Daw, N. D. (2019). Hippocampal contributions to model-based planning and spatial memory. Neuron, 102(3), 683-693.
[8] Simon, S. S., Yokomizo, J. E., & Bottino, C. M. (2012). Cognitive intervention in amnestic Mild Cognitive Impairment: a systematic review. Neuroscience & Biobehavioral Reviews, 36(4), 1163-1178.
[9] Stewart, R. (2012). Mild cognitive impairment—the continuing challenge of its “real-world” detection and diagnosis. Archives of medical research, 43(8), 609-614.
[10] Winblad, B., Palmer, K., Kivipelto, M., Jelic, V., Fratiglioni, L., Wahlund, L. O., ... & Petersen, R. C. (2004). Mild cognitive impairment–beyond controversies, towards a consensus: report of the International Working Group on Mild Cognitive Impairment. Journal of internal medicine, 256(3), 240-246.
[11] DeCarli C. Mild cognitive impairment: prevalence, prognosis, aetiology, and treatment. Lancet Neurol. 2003;2:15–21.
[12] Ward, A., Arrighi, H. M., Michels, S., & Cedarbaum, J. M. (2012). Mild cognitive impairment: disparity of incidence and prevalence estimates. Alzheimer′s & Dementia, 8(1), 14-21.
[13] Wei, E. X., Oh, E. S., Harun, A., Ehrenburg, M., & Agrawal, Y. (2018). Vestibular loss predicts poorer spatial cognition in patients with Alzheimer’s disease. Journal of Alzheimer′s Disease, 61(3), 995-1003
[14] Agrawal, Y., Smith, P. F., & Rosenberg, P. B. (2020). Vestibular impairment, cognitive decline and Alzheimer’s disease: balancing the evidence. Aging & mental health, 24(5), 705-708.
[15] Wei, E. X., Oh, E. S., Harun, A., Ehrenburg, M., & Agrawal, Y. (2017). Saccular impairment in Alzheimer’s disease is associated with driving difficulty. Dementia and geriatric cognitive disorders, 44(5-6), 294-302.
[16] Petersen, R. C. (2004). Mild underlying cognitive dysfunction as a diagnostic entity. Journal of Internal Medicine, 256, 183–194.
[17] Wilkins CH, Wilkins KL, Meisel M, Depke M, Williams J, Edwards DF. Dementia undiagnosed in poor older adults with functional impairment. J Am Geriatr Soc 2007; 55(11):1771–1776.
[18] Boustani M, Callahan CM, Unverzagt FW, Austrom MG, Perkins AJ, Fultz BA et al. Implementing a screening and diagnosis program for dementia in primary care. J Gen Intern Med 2005; 20(7):572–577.
[19] Morris JC. The Clinical Dementia Rating (CDR): current version and scoring rules. Neurology. 1993; 43: 2412–2414.
[20] Purser, J. L., Fillenbaum, G. G., Pieper, C. F., & Wallace, R. B. (2005). Mild cognitive impairment and 10‐year trajectories of disability in the Iowa established populations for epidemiologic studies of the elderly cohort. Journal of the American Geriatrics Society, 53(11), 1966-1972.
[21] Frigerio, C.S., Lau, P., Salta, E., Tournoy, J., Bossers, K., Vandenberghe, R., Wallin, A., De Strooper, B., “Reduced expression of hsa-miR-27a-3p in CSF of patients with Alzheimer disease,” (2013) Neurology, 81 (24), pp. 2103-2106.
[22] Yeh, S. C., Wang, J. L., Wang, C. Y., Lin, P. H., Chen, G. D., & Rizzo, A. (2014). Motion controllers for learners to manipulate and interact with 3D objects for mental rotation training. British Journal of Educational Technology, 45(4), 666-675.
[23] Prince, M., Bryce, R., Albanese, E., Wimo, A., Ribeiro, W., & Ferri, C. P. (2013). The global prevalence of dementia: a systematic review and metaanalysis. Alzheimer′s & dementia, 9(1), 63-75.
[24] Huansheng Ning et al. 2021. A Survey on Metaverse: the State-of-the-art, Technologies, Applications, and Challenges. arXiv:2111.09673 [cs.CY].
[25] Limperos, A., Waddell, T. F., Ivory, A. H., & Ivory, J. D. (2014). Psychological and physiological responses to stereoscopic 3D presentation in handheld digital gaming: comparing the experiences of frequent and infrequent game players. Presence: Teleoperators and Virtual Environments, 23(4), 341-353.
[26] Chávez, O. L., Rodríguez, L. F., & Gutierrez-Garcia, J. O. (2020). A comparative case study of 2D, 3D and immersive-virtual-reality applications for healthcare education. International journal of medical informatics, 141, 104226.
[27] Hepperle, D., Weiß, Y., Siess, A., & Wölfel, M. (2019). 2D, 3D or speech? A case study on which user interface is preferable for what kind of object interaction in immersive virtual reality. Computers & Graphics, 82, 321-331.
[28] Cockburn, A., & McKenzie, B. (2002, April). Evaluating the effectiveness of spatial memory in 2D and 3D physical and virtual environments. In Proceedings of the SIGCHI conference on Human factors in computing systems (pp. 203-210).
[29] S.-C. Yeh, Y.-Y. Li, C. Zhou, P.-H. Chiu and J.-W. Chen, "Effects of virtual reality and augmented reality on induced anxiety", IEEE Trans. Neural Syst. Rehabil. Eng., vol. 26, no. 7, pp. 1345-1352, Jul. 2018.
[30] A. Moreno, K. J. Wall, K. Thangavelu, L. Craven, E. Ward and N. N. Dissanayaka, "A systematic review of the use of virtual reality and its effects on cognition in individuals with neurocognitive disorders", Alzheimer’s Dementia Transl. Res. Clin. Intervent., vol. 5, no. 1, pp. 834-850, Jan. 2019.
[31] Hegarty, M. (2018). Ability and sex differences in spatial thinking: What does the mental rotation test really measure?. Psychonomic bulletin & review, 25(3), 1212-1219.
[32] Città, G., Gentile, M., Allegra, M., Arrigo, M., Conti, D., Ottaviano, S., ... & Sciortino, M. (2019). The effects of mental rotation on computational thinking. Computers & Education, 141, 103613.
[33] Nolte, N., Schmitz, F., Fleischer, J., Bungart, M., & Leutner, D. (2022). Rotational complexity in mental rotation tests: Cognitive processes in tasks requiring mental rotation around cardinal and skewed rotation axes. Intelligence, 91, 101626.
[34] Nah, F. F. H., Eschenbrenner, B., & DeWester, D. (2011). Enhancing brand equity through flow and telepresence: A comparison of 2D and 3D virtual worlds. MIs Quarterly, 731-747.
[35] Passig, D., & Eden, S. (2001). Virtual reality as a tool for improving spatial rotation among deaf and hard-of-hearing children. CyberPsychology & Behavior, 4(6), 681-686.
[36] Tsai, C. F., Chen, C. C., Wu, E. H. K., Chung, C. R., Huang, C. Y., Tsai, P. Y., & Yeh, S. C. (2021). A Machine-Learning-Based Assessment Method for Early-Stage Neurocognitive Impairment by an Immersive Virtual Supermarket. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 29, 2124-2132.
指導教授 葉士青 吳曉光(Shih-Ching Yeh Hsiao-Kuang Wu) 審核日期 2022-8-11
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