棒球擊球手為了打擊出更強勁的球，對於揮棒是否擊中甜蜜區很感興趣。本研究提供了一種偵測球與球棒的碰撞位置的方法，該方法將壓電薄膜感測器安裝在球棒的握柄，並在撞擊發生時感測球棒的振動訊號。本研究採用多項式回歸擬和曲線模型和決策樹回歸器模型，並透過特徵頻率以及經驗模態分解演算法提取出來的特徵來檢測擊球位置。在頻域訊號裡得到特徵頻率的峰值和比值，另外也將可以表示時域訊號趨勢的經驗模態分解殘差的斜率及其正負號提取出來作為模型的特徵。為了提高模型的檢測能力，本研究利用特徵重要性和統計方法，分別找出對偵測擊球位置最有影響力以及最高相關係數的特徵。木棒在固定撞擊實驗的平均絕對誤差(MAE)為0.26 公分，誤差容忍度小於1 公分時，準確度為95.4%。鋁棒在固定撞擊實驗的MAE 為0.35 公分，準確度為91.1%。而一般的情況下球棒碰撞的強度是未知的。為了驗證提出的方法具有檢測未知衝擊力的能力，本研究進行了實際擊球的實驗並收集數據。木棒在實際擊球實驗的平均絕對誤差(MAE)為0.71 公分，誤差容忍度小於1 公分時，準確75.6%。鋁棒在固定撞擊實驗的MAE 為0.75 公分，準確度為70.5%。

The baseball batters are interested in whether to hit the sweet zones because it can let the ball fly faster and farther. This study provides a detection method to detect the position of
ball-bat collision. In the proposed method, the piezoelectric vibration sensor is mounted on the knob of the bat, and the vibration signal of the bat is sensed when the impact occurs. In this study, the polynomial regression fitting curve model and the decision tree regressor machine learning model are applied to detect the hitting position via the features which were extracted
from the eigenfrequencies and empirical mode decomposition (EMD) algorithm. The values and the ratios of eigenfrequency peaks are generated in frequency-domain signal. The slope and its sign of the residual the EMD, which contains the directionality, are taken as the trend term of the time-domain signal, so they are treated as the features of the models. To improve the detecting ability of the models, the feature importance and the statistical methods are utilized to find the most important and the highest correlation coefficient for the impact position, respectively. The mean absolute error (MAE) of the stationary experiments of wooden bat is 0.26 cm, and the one of aluminum bat is 0.35 cm. The accuracy was 95.4% when the error tolerance was 1 cm for wooden bat and 91.1% for aluminum bat. In general, the intensity of the ball-bat collision is unknown. In order to confirm that the proposed method has the ability of detecting unknown impact force, the actual batting experimented were carried out and collected some data. The MAE of the actual batting experiments of wooden bat is 0.71 cm, and the one of aluminum bat is 0.75 cm. The accuracy was 75.6% when the error tolerance was 1 cm for wooden bat and 70.5% for aluminum bat.

Abstract i
1 Introduction 1
1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.3 Main Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.4 Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2 Literature Survey and Prerequisites 5
2.1 Sweet Zone of Baseball Bat . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.2 Natural Frequency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.2.1 Impact Position Effect . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.2.2 Gripping Method Effect . . . . . . . . . . . . . . . . . . . . . . . . 11
2.3 Different Methods of Detecting the Bat Hitting Position . . . . . . . . . . . . 11
2.3.1 Trigger Time of Sensor . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.3.2 Feature of Vibration Signal . . . . . . . . . . . . . . . . . . . . . . . 13
2.3.3 Comparison . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
3 Proposed Methodologies for Hitting Position Detection 19
3.1 Experimental Apparatus . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
3.2 Sensing Component and Equipment . . . . . . . . . . . . . . . . . . . . . . 22
3.2.1 Force Sensor (A301) . . . . . . . . . . . . . . . . . . . . . . . . . . 23
3.2.2 6-axis Sensor (ICM-20649) . . . . . . . . . . . . . . . . . . . . . . 24
3.2.3 Piezo Vibration Sensor (LDT0-028K) . . . . . . . . . . . . . . . . . 25
3.2.4 Oscilloscope (TBS2102B) . . . . . . . . . . . . . . . . . . . . . . . 26
3.3 Feature Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
3.3.1 The Largest Peak of the Fundamental Mode . . . . . . . . . . . . . . 27
3.3.2 Empirical Mode Decomposition . . . . . . . . . . . . . . . . . . . . 30
3.3.3 The Peak Values of the Spectrum . . . . . . . . . . . . . . . . . . . 35
3.3.4 The Ratio between the Eigenfrequency Peak Values . . . . . . . . . . 37
3.4 Statistical Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
3.4.1 Independent Sample T Test . . . . . . . . . . . . . . . . . . . . . . . 38
3.4.2 One-Way ANOVA . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
3.5 Decision Tree Regressor (DTR) . . . . . . . . . . . . . . . . . . . . . . . . 42
4 Dataset Collection and Implementation Results 43
4.1 Stationary Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
4.1.1 Dataset Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
4.1.2 Feature Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
4.1.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
4.2 Actual Batting Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
4.2.1 Experimental Setting . . . . . . . . . . . . . . . . . . . . . . . . . . 53
4.2.2 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
4.2.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
4.3 Comparison . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
5 Conclusion and Future Works 67
5.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
5.2 Future Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
Bibliography 69

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