近年來，虛擬實境（Virtual Reality, VR）技術逐漸發展成熟，被廣為應用於醫療、藝術、教育、建築及原型設計（prototyping）等諸多領域，在人機介面（human -machine interface）的原型設計上也開始有人利用沉浸式虛擬實境（immersive VR）輔助評估，以降低實體原型建置及再設計後重新建置所需花費的成本與時間。然而過去的研究指出虛擬實境中一直存在著空間壓縮（space compression）—人於虛擬環境中對於距離的估計顯著低於實際距離—的問題，即使虛擬實境的圖像品質、視野、即時追蹤系統技術已逐漸提昇，此深度感知（depth perception）的差異仍可能限制了虛擬評估的有效性與可行性。因此，本研究旨在基於過去研究的發現提出改善虛擬實境深度感知的方法，並透過實驗分析改善的成效、探討能否藉此提昇以沉浸式虛擬實境評估人機介面的可行性。
本研究提出改善虛擬實境中深度感知的「直接」方案（調整圖像尺寸比例）與「間接」方案（增加聽覺回饋），並鎖定「利用控制器移動物體」—如一般大眾交通運輸駕駛人員藉由操作控制面板上的按鍵將其所在之車輛移動至指定位置—的人機互動情境，設計互動實驗以評估兩方案的效果；但受限於實驗場景製作之難度，故將情境改成「人與載具不動、將目標朝著自己移動」、與實際情境為相對運動的作業。實驗共招募到 23名22至35歲、不具有虛擬實境操作經驗的男性研究參與者，且符合「矯正後或裸視視力達 1.0 」、「立體視覺能力正常」的篩選標準，以排除個體視覺能力差異可能造成的影響；但由於實驗設備問題導致兩位參與者中止實驗，故資料分析僅採納21位參與者之實驗結果。
實驗中請參與者採取坐姿，隨機執行「說出前方目標物與自己的距離」的口頭估計任務或「利用控制器將前方目標物移動至指定距離」的估計暨手動操作任務；除了在實體環境中與目標物、控制器互動外，亦比照真實場景建置具有相同視覺刺激的虛擬場景，同時也根據前述之直接、間接改善方案建置另外兩個虛擬互動場景。並透過頭戴顯示器、手勢感應器、以及三度空間遊戲引擎實現虛擬場景中的互動，在這四種場景當中，參與者都必須針對兩種目標距離（近或遠）、兩種控制器位置（近或遠）一一完成前述之口頭估計與估計暨手動操作任務，全程利用光學式動作擷取系統記錄參與者的上肢運動。
針對人機互動過程中的感知、認知、反應及績效等階段，分別使用估計準確度、動作特徵、吞吐量以及完成時間等做為評估指標，量化之指標透過重複測量變異數分析來探討不同場景、目標距離、控制器位置於各階段的差異；反應動作特徵則先根據關節中心座標的主成分分析結果定義動作姿勢的變異，再透過集群分析比較在不同場景、控制器位置下所做出的動作差異，其中因21位參與者中有一位手部成像問題影響其動作，故涉及動作之反應、績效階段分析結果僅採20人進行結果討論。
根據研究結果，雖然在訪談結果中多數參與者主觀認為虛擬與真實場景為高度相似，客觀指標仍顯示在虛擬環境中估計過程的感知及績效階段與真實環境下的顯著的不同，虛擬場景間則無顯著差異，其可能原因為在虛擬場景中的距離估計策略與在真實場景中有差異。在認知、反應階段則發現本研究提出之「直接」改善方案能夠顯著改善參與者在虛擬環境下近距離的深度感知錯誤情形，讓參與者於經調整比例的虛擬場景中之認知、反應能夠更貼近於真實。此外，透過動作特徵的分群結果發現，在虛擬環境中受限於觸覺回饋及手部動作追蹤、顯示的硬體限制，致使部分參與者在虛擬與真實環境中的互動仍存在著操作姿勢的差異。
本研究除了量化虛擬與真實場景之間的差異外，亦發現出調整虛擬環境圖像比例的改善方法較能夠使虛擬場景中的互動更接近真實互動，但整體上虛擬與真實之間仍未能夠有完全一致的互動結果，未來可藉由本研究提出之方法進一步探討不同比例的改善的效果差異，讓人機介面設計的模擬評估得以透過沉浸式虛擬實境技術忠實呈現。

In recent years, virtual reality(VR) technology has been gradually developed and is widely used in various fields such as medicine, art, education, architecture, and physical prototyping. In the prototype design of human-machine interface, the immersive virtual reality can be used as an evaluation method to reduce the cost and time required for physical prototyping. By using VR for design evaluation, the immersion provides users with an environment that allows complete human-machine interaction. However, it has been pointed out that there could be space compression in virtual reality. More specifically, distance estimated in the virtual environment could be significantly shorter than the actual distance. Even though the image quality, field of view, and real-time tracking technology have been gradually improved in virtual reality, the difference in depth perception may limit the effectiveness and feasibility of evaluation by VR. Hence, the aim of this study is to propose methods to improve the depth perception of virtual reality based on the findings of previous studies, and to analyze the improvement effect to investigate whether these methods can enhance the feasibility of immersive-virtual-reality-based evaluation for human-machine interface design.
In this study, a "direct" approach (image scaling) and an "indirect" approach (providing the auditory feedback about the distance from the object) were proposed to improve depth perception in VR. Besides, the scenario of human machine interaction in “using the controller to move the object,” such as the driver of general public transportation moves the vehicle to the specified position with the controller panel, was considered to design interactive experiments to evaluate the effect of the two methods of improvement. However, due to the difficulty of building experimental scenes, the situation is replaced by the relative movement, i.e. moving the target toward oneself, while the driver and the vehicle do not move at all. 23 male participants between 22 to 35 years old were recruited. They are required to have no experience with virtual reality and possess 20/20 vision (or corrected 20/20 vision) as well as normal stereo vision ability to exclude the possible influences of individual difference. However, two participants did not complete the experiment due to equipment problems, so only the experimental results of 21 participants were considered for data analysis.
Participants are required to perform a series of distance estimation tasks in a fixed position while sitting. One kind of tasks is verbal estimation, in which participants have to speak out the distance between the target in front and themselves. Another task is to estimate distance by using the controller to move the target to the specified distance. In addition to interacting with the target and controller in the real environment, the virtual scene with the same visual stimulus is built according to the real scene. Two other virtual interactive scenes are also built according to the direct and indirect improvement approach described above. The interaction in the virtual scene is achieved through a head-mounted display, a gesture sensor, and a three-dimensional game engine. In these four scenes, participants must complete the tasks with two target distances (near or far) and two controller positions (near or far), by performing both verbal and control-oriented estimation. The participants' upper limb movements were recorded throughout the entire process using an optical motion capture system.
For the phases of perception, cognition, reaction, and performance in the process of human-machine interaction, the estimation accuracy, motion features, throughput and completion time were used as evaluation indicators respectively. Quantitative indicators were used to explore the differences between different scenes, target distances, and controller positions at various stages through repeated measures of variance analysis. As for motion features, principal component analysis was conducted with joint center positions to define the variation of body postures, followed by the comparison of motion features between different scenes and controller positions through cluster analysis. The data of one of the 21 participants was excluded due to the limitation of hand tracking. So, in the reaction and performance phases, only 20 participants’ results were considered.
According to the results, although most participants think that the virtual and real scenes are highly similar during the interview, the objective indicators still show that the perception and performance phases of the estimation process in the virtual environment are significantly different from those in the real environment. However, there is no significant difference among the three virtual environments, which may be due to the difference in the strategies of distance estimation. It was also found that in the cognition and reaction phases, the direct improvement can significantly improve participants' depth perception in the virtual environment, and hence allows participants to experience cognition and reaction that are similar with those in the real environment. In addition, according to the clustering results of motion features, it was found that in the virtual environment, most of the participants still have different postures between the virtual and real environments. This may be caused by the limitations of haptic feedback, hand tracking, and hardware capability.
In addition to quantifying the difference in human-machine interaction between virtual and real scenes, it was also found that the improved method through image scaling can make the interaction in virtual scenes more similar with real interaction. Nevertheless, the interactions in virtual and real environments are not totally consistent. In the future, based on the improvement proposed in this study, the effects of various proportions of image scaling can be further investigated to enable more faithful simulated evaluation in immersive VR environments.

摘要..........I
第一章 緒論..........1
1.1 研究背景與動機..........1
1.2 研究目的與範圍..........5
1.3 研究架構與流程..........6
第二章 文獻探討..........9
2.1 人機介面..........9
2.1.1人機互動模型中的人類訊息處理..........9
2.1.2人機介面的評估..........11
2.2 虛擬實境..........12
2.2.1虛擬實境系統的分類..........13
2.2.2虛擬實境應用於介面設計評估：虛擬原型..........14
2.2.3虛擬與真實環境的差異..........15
2.3 深度感知..........19
2.3.1虛擬環境中影響深度感知及距離估計結果的可能因子..........20
2.3.2虛擬環境中深度感知的改善..........26
2.4 小結..........32
第三章 研究方法..........33
3.1 沉浸式虛擬實境中深度感知之改善方法..........33
3.1.1感知階段之「直接」改善..........34
3.1.2認知階段之「間接」改善..........34
3.2 實驗設計..........34
3.3 實驗任務..........36
3.4 實驗場景..........37
3.5 評估虛實差異之指標..........41
3.5.2認知..........41
3.5.3反應..........42
3.5.4績效..........43
3.6 實驗設備..........44
3.7 研究參與者..........47
3.8 實驗流程..........48
3.9 資料分析..........50
第四章 研究結果..........53
4.1 感知階段..........57
4.2 認知階段..........58
4.2.1估計準確度..........58
4.2.2相對估計誤差..........60
4.3 反應階段..........61
4.3.1吞吐量..........61
4.3.2動作特徵..........63
4.4 績效階段..........69
4.5 小結..........71
第五章 討論..........74
5.1 目標距離的影響..........74
5.1.1目標距離對認知階段之影響..........74
5.1.1.1認知階段之估計準確度..........75
5.1.1.2認知階段之相對估計誤差..........76
5.1.2目標距離不同對績效階段之影響..........79
5.2 控制器位置的影響..........81
5.2.1 控制器位置不同對反應階段之影響..........81
5.2.1.1 反應階段之吞吐量..........81
5.2.1.2 反應階段之動作特徵..........83
5.2.2 控制器位置不同對績效階段之影響..........99
5.3 立體視覺能力測驗結果的影響..........100
5.4 虛擬實境深度感知改善方法對人機互動過程的影響..........103
5.4.1感知階段之「直接」改善（調整虛擬環境圖像比例）..........104
5.4.2認知階段之「間接」改善（增加聲音回饋）..........104
第六章 結論..........106
6.1 研究主要發現..........106
6.2 研究貢獻與應用..........107
6.3 研究限制與未來方向..........109
參考文獻..........113
附錄一、研究倫理審查核可證明..........124
附錄二、各互動階段評估指標變異數分析表..........125
附錄三、自變項交互作用顯著之變數單純主效果分析表..........127
附錄四、雙眼深度視覺檢測說明..........128
附錄五、立體視覺能力高低組之獨立T檢定結果..........129

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