本論文開發一能夠即時處理在游泳時的動作追蹤演算法，此演算法提供的資訊包括游泳的趟數、划水次數及泳姿辨識。本論文主要由兩個分析階段組成，分別為加速度計資料處理階段和划水分析階段。首先，加速度計資料處理階段為收集三軸加速度計數值後，將不是游泳的浮動雜訊排除，再偵測是否開始游泳；接著，在划水分析階段為計算划水次數、游泳趟數與辨識泳姿。
從實驗結果顯示，我們提出的演算法於左右手的划水次數與游泳趟數及泳姿辨識之平均準確率分別為97.04%、96.73%、92.69%；市售運動錶(Garmin) 於左右手的划水次數與游泳趟數及泳姿辨識之平均準確率分別為92.15%、88.91%、82.24%。在實驗結果中本論文提出的演算法準確度高於其他論文的演算法與市售裝置，可證明使用本論文提出的演算法能夠有效的偵測游泳時的划水次數、游泳趟數及泳姿辨識。

This paper proposes an algorithm that can trace swimming algorithm, such as stroke counts, laps and swimming styles in real time. Our algorithm consists of two distinct parts. The first part is sensory data processing phase and the second part is stroke analysis phase. In the beginning of our algorithm, sensory data processing phase collects the data from triaxial accelerometer and eliminate floating signal. After that, our algorithm will judge whether the user starts to swim or not. Next part, stroke analysis phase, which computes stroke counts, laps and swimming styles.
The experiment revealed that the average accuracy of left and right hand with our algorithm in stroke counts is 97.04%, in lap counts is 96.73% and in swimming style recognition is 92.69%. The average accuracy of left and right hand with commercially available swim watch (Garmin) in stroke counts is 92.15%, in lap counts is 88.91% and in swimming style recognition is 82.24%. According to the experimental results, the algorithm, proposed by this paper, has a higher accuracy than other devices and other algorithms. Therefore, our algorithm can efficiently compute stroke counts, laps and swimming styles in real time.
According to the experimental results, the algorithm, proposed by this paper, has a higher accuracy than other devices and other algorithms. Therefore, our algorithm can efficiently compute stroke counts, laps and swimming styles in real time.

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