要將虛擬實境（Virtual Reality, VR）推廣至更多人使用，勢必得舒緩使用虛擬實境時會遭遇之限制。使虛擬實境使用族群增加緩慢的重要因素在於運行虛擬實境時，常需搭配其他硬體設備，追求即時的互動與真實的體驗。例如：利用紅外線感測器定位玩家手部位置並偵測其動作。為了搭配這些硬體設備並顯示擬真的畫面，運行虛擬實境的設備需求也會隨之提高，這不僅是增加了使用者的負擔，更導致想體驗虛擬實境領域的使用者卻步。
　　為舒緩上述限制，本研究之目標是讓使用者能使用最低的硬體需求即可體驗真實的虛擬實境。本研究提出HandVR用來解決虛擬實境常需搭配額外硬體設備之限制的方案。首先，為取代虛擬實境玩家手部定位與動作辨識之硬體設備，本研究使用MediaPipe Hands深度學習（Deep Learning）模型作為取得玩家手部資訊之來源。此模型以RGB圖像作為輸入，經過運算後，輸出手部共21個關鍵點之三軸座標，再以此結果計算各個手部關節點角度，並模擬雙手在虛擬實境中的姿態與手勢；再來，為同時減緩運行平台之負擔，本研究除了使用輕量化版本的深度學習模型外，也使用先檢測手掌、再進一步分析手部關鍵點的方式，以減輕運算的需求與負擔。HandVR以上述兩大技術為重點，實現僅需使用手機即可運行、使用雙手操作之虛擬實境遊戲。
　　本研究結合MediaPipe Hands深度學習模型與Google Cardboard頭戴顯示器，並使用Unity做為開發用的遊戲引擎，設計了一款能在Android手機運行的虛擬實境、且不須架設伺服器之遊戲──HandVR，遊戲中包含了記憶力遊戲與休閒大廳兩種關卡類型，能夠讓使用者體驗手勢辨識結合虛擬實境之特色；HandVR驗證了可行性與可玩性之價值，而在達到大幅降低使用虛擬實境門檻的同時，達成了本研究欲推廣虛擬實境之目的。

As for promotion of virtual reality, it needs to alleviate the restrictions encountered when using VR. Among those restrictions, the important factor that slows down the increase in the use of virtual reality is when running virtual reality, additional hardware is needed to pursue real time interaction and real experience. For example, infrared sensors are used to locate the player’s hand position and detect its movements; however, in order to match these hardware devices and display immersive scenes, the demand for VR devices will also increase. This not only increases the burden on users, but also makes users who want to experience the VR deterred.
In order to alleviate the above limitation, the goal of this study is to allow users to have a reality experience with the lowest hardware requirements. HandVR is a solution proposed in this thesis to remove the additional hardware devices. First of all, to replace the hardware equipment commonly used in VR for capturing player's hand position and motion recognition, we use MediaPipe Hands deep learning model which takes RGB images as input, and outputs the three-axis coordinates of a total of 21 key points on the hand for obtaining player hand information, and then calculates the angle of each hand joint point, and simulates the postures and gestures of both hands in VR. Second, to reduce the computational burden at the same time, in addition to using a lightweight version of the deep learning model, we also use the method of first detecting the palm and then further analyzing the key points of the hand to reduce the need and burden of computing. HandVR focuses on the above two technologies, and achieves VR games that can be run only by using a mobile phone and operated by hands.
This study combines the MediaPipe Hands deep learning model with the Google Cardboard head-mounted display, and uses Unity as the game engine for developing and designing a VR game that can run on Android phones without setting up a server: HandVR. The game contains two types of levels: memory games and leisure halls, allowing users to experience gesture recognition combined with the characteristics of VR. HandVR has verified the value of this study in potential to be promoted, feasibility, and playability. While achieving a significant reduction in the demands for using VR, it also achieves the purpose of this study to promote virtual reality.

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