本研究的目的為使用慣性感測器(IMU)在落地評分錯誤系統(LESS)上實踐，並驗證不同分析方式計算/預測(量測)到的運動學參數，針對不同時間點量測的數值做信效度的分析，並將量測的運動學參數用在LESS自動量化給分程式中進行給分，最後與不同分析方式的自動量化之LESS分數相比，哪一種分析方式與專家分析給出的結果最接近。本研究招募了19名受試者，健康男性13名、女性6名，男性前十字韌帶1名、男性前十字韌帶重建1名，本研究並沒有針對族群的差異進行探討所以不進行族群分組。使用三維動作分析系統(Qualisys)、慣性感測器(Delsys Avanti)及高速攝影機(GoPro)來收集受試者在跳躍落地時的運動情形，透過四種分析方式(二維影像、三維動作、人體姿勢骨架及IMU預測模型)來量測關節角度，最後將得到的關節角度放入MATLAB自動給分程式進行LESS給分。並使用了組內相關數(ICC)、布蘭德-奧茲曼圖及無母數統計-威爾卡森符號檢定進行四種分析方式在不同時間點(第一次落地(IC1)、最大膝關節屈曲(MKF1)、兩個時間的角度差異(ROM))的矢狀面關節角度量測數值之信效度。除了在第一次實驗的單腳落地之軀幹在IC1的IMU預測模型、第二次實驗的雙腳落地之左側髖關節在IC1時二維影像及左側膝關節在IC1時的人體姿勢骨架呈現差的相關性以外其餘的分析方式量測角度都有中等至極好的相關性。觀察布蘭德-奧茲曼圖上可以發現大多數分析方式與三維動作相比，量測的角度誤差都有落在1.96倍的標準差範圍內，與三維動作相比，在第二次實驗時二維影像分析的右側單腳落地的軀幹角度、人體姿勢骨架分析在雙腳落地時髖關節與膝關節角度以及在第一次與第二次實驗的IMU預測模型分析雙腳落地時髖關節與膝關節較沒有顯著差異，其餘的分析方式量測的角度多數呈現顯著差異(p<0.05)，自動評分系統的評分表現以右側雙腳落地時不同分析自動評分系統表現與專家評分相近，IMU預測模型給分的結果也比人體自姿勢骨架接近專家給分。在IMU預測模型進行LESS給分時，膝關節外翻、站立時寬度及整體印象的觀察等項目無法透過關節角度去判斷之外，其餘的項目都可以實現LESS評分。未來用自動化評分時，可以針對使用的族群設定適合的閾值，在合理的閾值下可以使LESS用於預防傷害的功用發揮到最大。

The purpose of this study is to use the Inertial Measurement Unit (IMU) to practice the LESS system, and verify the kinematics parameters calculated/predicted (measured) by different analysis methods. The reliability and validity of the measured values at other time points are analyzed, and the measured kinematic parameters are used in the LESS automatic quantification program for scoring. Finally, compared with the fewer scores automatically quantified by different analysis methods, which analysis method is closest to the results given by expert analysis? In this study, 19 subjects were recruited, including 13 healthy men and 6 women, 1 male with anterior cruciate ligament reconstruction and 1 male with anterior cruciate ligament reconstruction. This study did not discuss the difference in group effect, so there was no racial division. A three-dimensional motion analysis system (Qualisys), IMU sensor (Delsys Avanti), and high-speed cameras (GoPro) are used to collect the movements of the subjects during the jump landing task. Four analysis methods (2D image, 3D motion, human posture skeleton tracking, and IMU prediction model) were used to measure the joint angles. Finally, the obtained joint angles are input into MATLAB, and the subprogram is scored automatically. Intraclass Correlation Coefficient (ICC), Bland-Altman plots, and Non-parameter Statistics: Wilcoxon Signed-rank Test were used to analyze the reliability and validity of sagittal joint angle measurements at different time points (Initial contact (IC), Maximum Knee Flexion (MKF) and the angle difference between IC and MKF (ROM)). Except for the IMU prediction model of the trunk with unilateral landing in IC1 in the first experiment, the 2D image of the left hip joint with bilateral landing in IC1 in the second experiment, and the poor correlation of the human posture skeleton of the left knee joint in IC1, all the other analysis methods have moderate to excellent correlation. By observing the Bland-Altman plots, it can be found that compared with the 3D motion, the measured angle value error of most analysis methods falls within the standard deviation of 1.96 times. Compared with the three-dimensional movement, the two-dimensional image analysis of the torso angle of the right single foot landing in the second experiment, the human posture skeleton analysis of the hip and knee joint angles when the two feet landed, and the first and second experiments. There was no significant difference between the hip joint and the knee joint when the two feet landed on the IMU prediction model, and most of the angles measured by the other analysis methods showed significant differences
III
(p<0.05). The scoring performance of the automatic scoring system is similar to the expert scoring based on different analyses when the right-side bilateral, and the results given by the IMU prediction model are also closer to the expert scoring than the human body self-pose skeleton. When the IMU prediction model is used for LESS scoring, items such as knee valgus, standing width, and overall impression cannot be scored by the joint angle, and the rest of the items can achieve LESS scoring. In the future, when automatic scoring is used, appropriate thresholds can be set for the groups used, and under a reasonable threshold, the function of LESS for injury prevention can be maximized.

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