Advanced RISC Machine (ARM)是一個32位元精簡指令集處理器架構，其廣泛地使用在許多嵌入式系統設計，此處理器符合其主要設計目標為低成本與低耗電的特性，至2009年為止，ARM架構處理器占了市面上所有32位元嵌入式RISC處理器90%的比例，使它成為占全世界最多數的32位元架構之一。而本論文在此嵌入式系統上開發手勢辨識，如此一來任何只要裝有接收器的3C產品，使用者只需要遠距離利用手勢的方式即可代替一般遙控器或觸控的功能，並達到方便、省時與低成本的功效。
一般手勢辨識主要分為三個部份，分別為手部定位、手部追蹤與手勢辨識，在本論文中提出了相似比率法與Harris偵測法來完成手部定位，並以改良混和Histogram of Oriented Gradients (HOG)與Mean-shift方法來進行手部追蹤的效果，最後再以temporal difference與掃描投影法達成手勢辨識。經由實驗的結果得知，本論文所提出之方法可成功的在較低效能的硬體上即時執行，並可在受干擾的環境下完成手勢辨識指令。

The paper develop the gesture recognition in embedded system ARM. As long as any 3C products with receiver equipped, users can only need to use gestures to control machines instead of remote controllers or touch functionality. It reaches convenient, time-saving and low-cost effectiveness.
General gesture recognition is divided into three parts: hand detection, hand tracking and gesture recognition. This paper proposes similar ratio method and Harris detection method to complete the hand detection, and hybrids Histogram of Oriented Gradients (HOG) and Mean-shift approach to hand tracking results, and finally the paper uses temporal difference and scanning projection method to reach gesture recognition. At last, according to the results of the experiment, the proposed method reach 89% correct rate for gesture recognition. It can successfully implement in real time in the lower performance hardware, and can achieve gesture recognition commands under disturbed environments.

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