手勢辨識已經逐漸成為生活中重要的人機互動介面。但是，很少有研究討論透過機器學習建立個人化手勢軌跡辨識模型。本論文利用Wii Remote以及紅外線光筆為硬體工具，建立一套具有手勢擷取、自動特徵分析、個人化手勢模型建立功能的系統。使用者可以經簡單的訓練過程，透過機器學習建立專屬於自己的個人化手勢辨識模型，利用客製化手勢指令使用上直覺、簡單、短暫、具有獨特性之特點，減少學習、適應的時間，更快發揮統效益，減少人為操作所產生的系統錯誤。
本研究共可辨識12種手勢，經10位參與實驗的使用者測試後完全訓練平均正確率達93%，標準差5.69%；實驗驗證後六位使用者之平均正確率為74.995%，標準差16.72%。

Gesture has become an important Human Computer Interaction (HCI) in daily life. Yet seldom research focus on building personalized hand-gesture recognizing model. In this thesis, a system with hand position tracking and gesture feature capturing was proposed for adapting personalized gesture model. Thru a simple and short training process, each user can build a characteristic personalized model of gesture recognizing. With this model users can enjoy the intuitional, simple, fast and unique hand gesture interface which reduce learning and adapting time and decrease the system error due to improper human manipulation.
In this thesis, 12 different hand gestures are identified successfully. By using Wii Remote and customized IR light pen, 10 participants have a 93% correct model training rate, and 6 out of these 10 users have a 75% correct recognizing. Therefore, by deploying the system daily life with household electric appliances can be more convenient and more ergonomic.

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