English  |  正體中文  |  简体中文  |  全文筆數/總筆數 : 80990/80990 (100%)
造訪人次 : 41264233      線上人數 : 656
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
搜尋範圍 查詢小技巧:
  • 您可在西文檢索詞彙前後加上"雙引號",以獲取較精準的檢索結果
  • 若欲以作者姓名搜尋,建議至進階搜尋限定作者欄位,可獲得較完整資料
  • 進階搜尋


    請使用永久網址來引用或連結此文件: http://ir.lib.ncu.edu.tw/handle/987654321/86313


    題名: 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
    作者: 黃程意;Huang, Cheng-Yi
    貢獻者: 軟體工程研究所
    關鍵詞: 注意力不足過動症;評估;虛擬實境;機器學習;深度學習;attention deficit hyperactivity disorder;assessment;virtual reality;machine learning;deep learning
    日期: 2021-08-24
    上傳時間: 2021-12-07 12:31:03 (UTC+8)
    出版者: 國立中央大學
    摘要: 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。;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.
    顯示於類別:[軟體工程研究所 ] 博碩士論文

    文件中的檔案:

    檔案 描述 大小格式瀏覽次數
    index.html0KbHTML135檢視/開啟


    在NCUIR中所有的資料項目都受到原著作權保護.

    社群 sharing

    ::: Copyright National Central University. | 國立中央大學圖書館版權所有 | 收藏本站 | 設為首頁 | 最佳瀏覽畫面: 1024*768 | 建站日期:8-24-2009 :::
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - 隱私權政策聲明