前言：近年來許多的研究將監控訓練負荷量做為減少球員運動傷害的方法，因為過度的訓練可能會影響球員的運動表現，甚至產生運動傷害，而排球是項跳躍十分頻繁的非接觸性運動，女性排球員比男性有更高的前十字韌帶 (ACL) 傷害風險。而監控短期與長期訓練負荷比 (ACWR) 可以了解球員當前的訓練負荷(短期) 以及球員已儲備的訓練負荷 (長期)。ACWR 有兩種算法：(1) 滾動平均值 (RA) 和 (2) 指數加權移動平均值 (EWMA)，以利教練團能監控量化球員體能狀態及了解選手隨後運動傷害風險。目的：使用LPS結合IMU 測量 Player load、移動距離和跳躍次數作為外在活動量的監控參數，並透過 RA 與 EWMA兩種 ACWR算法監控大專女子排球選手的訓練負荷量比值，比較兩種算法與實際發生運動傷害之間的關聯，方法：實驗對象為15名大專院校甲級女子排球隊公開一級選手，選手常規熱身結束後，使用已同步LPS與IMU 放置於運動背心左側肩膀暗袋處，蒐集Player load、移動距離和跳躍次數等數據。結果：RA 與 EWMA 兩種算法為中度正相關 (r ＝0.400, p ＜.001)，RA 算法最高值皆出現在第五週，隨後幾週都有較大幅度的下降，而 EWMA 則第五週多為次高比值，最高值出現在第六週，隨後幾週則呈現逐步的下降，而在第七週選手 L2 產生非接觸急性傷害。結論：EWMA 比值較 RA 更貼切實際產生傷害時間段，可以了解選手當下體能狀況及隨後產生傷害的風險，但不是預測傷害的工具。

Foreword: In recent years, many studies have used monitoring of training load as a method to reduce players' sports injuries, because excessive training may affect players' sports performance and even cause sports injuries, and volleyball is a non-contact sport with very frequent jumping. , female volleyball players have a higher risk of anterior cruciate ligament (ACL) injury than males. Monitoring short-term and long-term training load ratio (ACWR) can understand the player's current training load (short-term) and the training load the player has reserved (long-term). ACWR has two algorithms: (1) rolling average (RA) and (2) exponentially weighted moving average (EWMA) to facilitate the coaching staff to monitor and quantify players' physical status and understand the player's subsequent sports injury risk. Purpose: Use LPS combined with IMU to measure player load, moving distance and number of jumps as monitoring parameters for external activity, and monitor the training load ratio of college female volleyball players through two ACWR algorithms, RA and EWMA, and compare the two algorithms with The correlation between actual sports injuries. Method: The experimental subjects were 15 open first-level players of the Class A women's volleyball team of colleges and universities. After the players' regular warm-up, synchronized LPS and IMU were used and placed in the left shoulder pocket of the sports vest collecting data such as Player load, moving distance, and number of jumps. Results: The RA and EWMA algorithms have a moderate positive correlation (r = 0.400, p <.001). The highest values of the RA algorithm appeared in the fifth week, and then declined significantly in the following weeks, while the EWMA algorithm The ratio was the second highest for more than five weeks, with the highest value appearing in the sixth week, and then gradually declining in the following weeks. In the seventh week, player L2 suffered a non-contact acute injury. Conclusion: The EWMA ratio is more relevant to the actual time period of injury than RA. It can understand the player's current physical condition and subsequent risk of injury, but it is not a tool to predict injuries.

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