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標題: Development of a simple force prediction model and consistency assessment of knee movements in ten-pin bowling
作者: 洪崇舜
Hung, Chung-Shun
關鍵字: 保齡球;Ten-pin bowling;下肢;LabVIEW;類神經網路;Lower Extremities;LabVIEW;Artificial Neural Network
出版社: 生物產業機電工程學系所
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High performance in the sport of bowling requires accurate movement, steady motion, and adequate direction and usage of forces by the bowler. The aim of this research was to use LabVIEW to help bowlers understand their joint movements, forces acting on their joints, and the consistency of their knee movements while competing in ten-pin bowling. Kinetic and kinematic data related to the lower limbs were derived from bowlers’ joint angles and the joint forces were calculated from the Euler angles equations and the inverse dynamics method with Newton-Euler equations. A modular program was designed in LabVIEW, and the bowlers’ accuracy and consistency were interpreted through data presentation.
In order to apply in bowling alley without force plate, this research focused on knee joints for ten-pin bowling and developed an artificial neural network (ANN)-based data-driven model for predicting the knee forces from Euler angles. The network comprises one input, one hidden, and one output layer. It was trained using a back-propagation algorithm. 3D Euler angles and 3D joint forces of knee were input and output parameters, respectively, of the model. Thus, the model was a unique structured ANN model whose weight parameters were characteristic of each bowler’s personal style. Correlation coefficients were computed after ANN trained and all values exceeded 0.9. This result implies a strong correlation between the joint angles and forces. Furthermore, the predicted 3D forces (obtained from ANN simulations) and the measured forces (obtained from force plates via the inverse dynamics method) are strongly correlated. The agreement between the predicted and measured forces was evaluated by the coefficient of determination (R2), which reflects the bowler’s consistency and steadiness of the bowling motion at the knee. The R2 value was beneficial in assessing the consistency of the bowling motion. The R2 value close to 1 shows a more consistent sliding motion. This research enables the prediction of the forces on the knee during ten-pin bowling by ANN simulations using the measured knee angles. Consequently, coaches and bowlers can use the developed ANN model and the analysis module to improve bowling motion.

保齡球運動是講求動作的準確性、穩定性與力量表現的運動,本研究欲以科學的方法分析求得相關參數,協助保齡球選手了解保齡球運動下肢的移動、受力與動作一致性的情況。透過收集8位俱樂部保齡球選手的三維(Three dimensional)運動學與作用力板資料,利用尤拉角與逆動力學計算關節角度與作用力,並以LabVIEW建構運算整合程式,透過資料的呈現了解選手動作的一致性。
保齡球下肢膝關節為本研究主要觀察的目標,運用類神經網路(Artificial Neural Network, ANN)的倒傳遞演算進行估算作用力,此網路包含一個輸入層、一個隱藏層與輸出層,輸入參數為三維尤拉角度,輸出目標為三維關節作用力,如此可針對不同選手個人姿勢建構各自類神經網路與加權參數,學習訓練後計算其相關係數(r)高於0.9以上,即可說明角度與作用力有高度相關,此設計的ANN model 可被用以估算膝關節作用力,且其預測三維作用力與實驗結果比較,可發現作用力變化情況是相同的,再將此ANN模擬與實際試驗資料求確定系數(Coefficient of determination, 即R-square),R-square可評估選手動作的一致性程度;在膝關節之平均R-square值越接近1,則表此選手膝關節運動較為一致。因此本研究針對保齡球運動選手下肢作用力做預測,並將量測得到的膝關節角度輸入ANN模擬來推測作用力大小,未來可應用於比賽或球場上,其R-square值評估膝關節作用力一致性,進而評估下肢動作一致性,因此教練與選手可藉此分析系統的結果改善動作。
其他識別: U0005-2901201313555200
Appears in Collections:生物產業機電工程學系

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