腦中風經常造成日常生活功能障礙，須積極配合治療，才能有效改善運動功能。研究顯示，大量的患側上肢持續動作練習，可增強中風患者學習的效果。一般傳統復健治療，須對患者進行一對一的治療訓練，費力且缺乏量化及客觀評估。因此本研究目的為發展居家式上肢復健輔助訓練系統，提供中風患者於家中也能進行復健訓練，減少醫療成本與往返醫院的不便，並能不受時空限制而經常復健，增進醫療效果。
本研究以機械手臂提供上肢中風患者居家復健之功能。機械手臂使用兩顆AI馬達來驅動連桿，提供水平的二維運動，以Visual Studio 2010撰寫人機操作介面，功能主要分成三個訓練模式:主動模式，被動模式及阻力模式。主動模式由機械手臂帶動患者上肢，作不同軌跡與速度之訓練，並記錄使用者的上肢末端位置與速度，及時提供視覺回饋資訊給使用者；被動模式由患者執行特定平面軌跡，並計算實際移動路徑與該軌跡之誤差，以均方根誤差作為患者復健效果的評估依據，以此功能來提升與評估自主控制能力；阻力模式則提供分級阻力，讓患者以嘗試推動該阻力，來鍛鍊並提升肌力。最後以肌電訊號，即時反應力的使用情形並將訊號以MATLAB處理計算出中位頻率，由該頻率變化情形來評估患者運動練習後之肌肉疲勞狀況，預防過度使用而造成的運動傷害。

Neurological impairments resulted from strokes always lead to functional disabilities, but it is possible for the patients to recover through proper treatments and exercise. However, traditional treatment is labor-intensive and limited to subjective assessment. With technology advances, robot-aided training systems have been developed to provide movement therapy and quantitative assessment. Therefore, in order to reduce health-care costs and inconvenience of patients, and to provide a regular exercise free of space and time limitation, a home-care rehabilitation training system was designed and implemented.
The system consists of a 2-dimensional manipulator. AI motors were used to drive the links and to record the position and velocity of the manipulator via their encoders. The system GUI was programmed in Visual Studio 2010, and including three functions: (1) Manipulator is used to provide different 2-D movements under different velocities; (2) Users are asked to track trajectory shown on the monitor to enhance their control ability; (3) Graded resistance is provided, by overcoming the resistance to improve muscle strength. In addition, Electromyography (EMG) was applied to measure fatigue as it is widely used for evaluating electrical activity produced by skeletal muscles. The signals were analyzed to detect muscle fatigue so as to avoid injuries through digital signal processing.

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