本研究藉由肌電訊號、磁性編碼器、慣性感測器所得的訊號，透過機器學習的方法，建立人體在運動時的模型並判斷姿態，進而下達命令給馬達提供正確的輔助力。膝關節輔具使用特殊設計的四連桿，以配合腿部運動軌跡。透過雙眼視差法，使用網路攝影機取得腿部運動時的姿態，匯入Matlab建立各姿態時腿部的三維空間座標，再使用差分進化演算法(Differential Evolution)，求得最佳四連桿瞬心軌跡。最後，量測腳上的肌電訊號、大腿及上半身的慣性感測器、膝關節四連桿上的磁性編碼器，透過機器學習判斷當下姿態，作為輔具馬達出力的參考訊號。

This paper developed a method to design a four-bar linkage for the knee. We use two USB-camera to capture knee motion. With those date , using Matlab can calculate the instantaneous center(I.C.) of knee. Once obtain I.C. trajectory, we design an objective function and use initial condition to generate four-bar linkage. By comparing generated trajectory with measured trajectory, objective function will output an error. This error will be the index of Differential Evolution(DE). Finally, optimal four-bar linkage can be obtained with DE. This optimal four-bar linkage will assemble on the knee with reduction gear and servo motor. We also design an Electromyography(EMG) device to detect electrical activity produced by muscles. Additionally, using inertial measurement unit(IMU) to measure body angle and angular velocity which can tell us body position. Last but not least, an encoder will calculate the degree of knee by assembling on the reduction gear. By using these signal as input ,we will train a model which will output an index that tells servo motor how much torque to generate.

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