排球攔網動作之下肢肌肉模擬

Lower Extremity Muscles Modeling of the Pre-Blocking Movement in Volleyball

10.3966/207332672016061301001

王奕霖(I-Lin Wang)；陳五洲(Wu-Chou Chen)；張維綱(Wei-Gang Chang)

骨骼肌肉模型 ； 動力學逆過程 ； 肌肉模型驗證 ； muscles modeling ； musculoskeletal model ； inverse dynamics

華人運動生物力學期刊

13卷1期（2016 / 06 / 01）

1 - 10

繁體中文

目的：本研究在探討排球攔網起跳動作下肢肌肉模擬，預期建立排球攔網動作骨骼肌肉模型，並以表面肌電驗證所建立的肌肉骨骼模型。方法：本研究採用1 名大學優秀女子排球選手為受試者。9臺Vicon Mcam2 光學攝影機 (250Hz) 、2 塊三軸測力板 (1000 Hz) 和表面肌電同步擷取資料，以Matlab 和Anybody 軟體進行資料處理，並以動力學逆過程算得下肢內在動力學參數。結果：排球攔網起跳動作主要作用肌群為股外側肌、股內側肌、股直肌、脛前肌和腓腸肌群，本研究結果顯示起跳動作右腳髖關節最大力矩約119.5Nm，左腳髖關節最大值力矩約80.5Nm；右腳膝關節最大值力矩約101.1Nm，左腳膝關節最大值力矩約106.4Nm；右腳踝關節最大值力矩約76.1Nm，左腳踝關節最大值力矩約67.3Nm。表面肌電與模擬數值趨勢相同的有股二頭肌、股內側肌、股外側肌、股直肌、腓腸肌外側頭、腓腸肌內側頭，趨勢不相同的有半腱肌、半膜肌、脛骨前肌。結論：本研究使用運動學資料驅動肌肉骨骼模型，發現預測值與表面肌電的訊號有密切的關係，並驗證所建立的骨骼肌肉模型符合實際動作肌肉活化情形，但模擬計算上並未考量到肌肉共同收縮的特性，在未來研究上須列入考量，所模擬出來的骨骼肌肉模型，未來在未黏貼表面肌電的情況下，可以用來預測受試者在做動作時肌肉活化的情形，作為排球訓練上的參考。

Purpose: This study was development and validation of a three-dimensional biomechanical model of the lower extremity pre-blocking preparation movement in volleyball. Methods: Kinematics and ground reaction force data were synchronized to obtain from one volleyball player, using nine Vicon Mcam2 cameras (250 Hz) and two force palates (1000 Hz). Data were analyzed by using the Matlab and Anybody analysis software. An inverse dynamic process was used to calculate the kinetically parameters for the lower extremity. Results: During the take-off period right hip maximum torque of about 119.5 Nm. Left hip maximum torque of about 80.5 Nm. The maximum torque of the right knee about 101.1 Nm. Left knee maximum torque of about 80.5 Nm. The maximum torque of the right ankle about 76.1 Nm. Left ankle maximum torque of about 67.3 Nm. Surface electromyography and muscle simulate the same trends:biceps femoris, vastus medialis, vastus lateralis, rectus femoris, lateral gastrocnemius triceps, medial head of gastrocnemius muscle, the trend does not have the same semitendinosus, semimembranosus, tibialis anterior. Conclusions: This study used kinematic data to drive musculoskeletal model and found that the predictive value of the surface EMG signals are closely related, and validate the model established skeletal muscle movement muscle activation realistic situations, but not consideration the characteristics of muscle co-contraction. It maybe needs into account in the future research, the simulated model of skeletal muscle. The case in the future without adhesive surface electromyography. It can be used to predict the situation muscle activation,as also refer on the volleyball training.

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