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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/65680

    Title: 虛擬教室結合頭戴式顯示器之注意力偵測設計及準確度分析與研究
    Authors: 張淵順;Chang,Yuan-Shuen
    Contributors: 資訊工程學系
    Keywords: 虛擬教室;頭戴式顯示器;注意力;穿戴式感測器;Virtual classroom;HMD;Attention;Wearable Sensor
    Date: 2014-07-28
    Issue Date: 2014-10-15 17:08:09 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 虛擬教室結合頭戴式顯示器的系統是目前認知復健上相當有前景的一項虛擬實境發展與應用,由於學習的成效好壞和個人的注意力集中與否有相當大的關係。因此藉由與虛擬教室中人物的互動學習,能夠將注意力不集中的行為矯正回來。注意力好的學習者會將大部分的注意力集中於老師或是白板、投影幕的教材上,反之,注意力差的學習者容易被課堂上其他學生的舉動所影響,導致其無法集中注意力於學習上。為了能夠在虛擬教室系統中了解受試者的注意力所在,本研究透過3D頭戴式顯示器做以科技輔助來為受試者呈現出虛擬實境的教室場景
    ;The Virtual Classroom (VC) with the head-mounted display (HMD) is a quietly promising development and application in virtual reality for the cognitive rehabilitation nowadays. Since the effect of learning has an important relationship with personal attention focus, using the interactive learning with virtual characters in the VC enables to redress the behavior of patients who have attention deficit. Good learners can pay attention and focus on a teacher, a whiteboard or a projection screen, however, learners with attention deficit are easy to be influenced by other students’ movement and behavior, so that they cannot concentrate on learning.
    Therefore, understanding what students look at is a very important thing. In order to find out the targets students care, in this study, a virtual classroom system through HMD has an innovative method called the attention ring that can detect their attention. Due to a high price of commercially available HMD with eye-tracking, this study designs an attention detection method with a wearable sensor to overcome the attention detection problem, so that the VC system can be popularized for general education researchers.
    This study collects 30 healthy participants to attend the virtual classroom attention experiment. During the experiment, every participant listens to the researcher’s instructions to look at the specific targets in the VC. The VC system gives two outputs about the data of the attention ring and the degrees of head movement rotation for post-hoc analysis. As a result, the accuracy of attention detection leads to upon 80%, and using the SVM analysis to improve overall accuracy is verified.
    Appears in Collections:[資訊工程研究所] 博碩士論文

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