臺灣博碩士論文加值系統

有動作障礙的患者大多需要每週2次以上的復健，而因為中風造成動作障礙的患者數隨著人口的老年化上升，為了顧全患者的復健品質，卻造成更多的醫療人力資源消耗。因此，居家復健的發展也是現在需要重視的領域。
本研究的目的在於開發一套適合患者自行使用的復健動作監控系統，讓患者在沒有治療師在旁指導輔助的情況下，依然可以透過系統的虛擬實境畫面與動作判斷的語音回饋來引導修正錯誤的動作方向，提高主要動作的關節活動角度並降低偏移角度。藉由網路架設資料庫來連接病患與醫院端資料雙向傳輸，讓治療師可以遠端監控患者復健情況，維持患者的復健品質也節省治療師人力。
此基於虛擬實境的穿戴式復健動作監控系統與馬達驗證比對後，在X、Y和Z軸的平均誤差為1.01°、0.5°及1.1°。此外，在臨床實驗驗證上，兩位使用本系統的患者經過10次復健系統訓練後，在偏移程度減少的軸向相較未使用本系統執行復健訓練患者多，顯示本系統在中風患者的復健上有給予實際復健成效。

Most of the patients with movement disorders need rehabilitation twice a week for recovering, and the population of stroke patients with movement disorders is raising, which is caused by aging of the population. It makes the demand for therapists is increasing for keeping high qualities of rehabilitation. Therefore, home-based rehabilitation is out of the solution to solve these problems.
The purpose of this study is developing a self-use rehabilitation movement monitoring system for patients. The system provides virtual reality and voice feedback to guide the patients to decrease wrong orientation of movement angle and enhance correct orientation angle without therapists’ supervision. The angular position of limbs during rehabilitation process will be recorded and uploaded to the database. Then, the therapist is able to download the data to analyze the result and adjust the data description via internet. Through the wireless communication is able to save manpower of therapist and maintain rehabilitation quality.
The system has been verified with single axis motor. The average error is 1.01, 0.5 and1.1 degree, respectively, for X, Y, Z axis. Based on the experiment results obtained from stroke patients, through involving 10 times clinical training for each participant, the proposed system is certified as an efficient platform for helping the participants to improve their upper limb movement.

摘要 I
ABSTRACT II
誌謝 III
目錄 IV
圖目錄 VI
表目錄 IX
第一章、 緒論 1
1.1 動機與目的 1
1.2 文獻探討 2
1.3 論文架構 6
第二章、 背景與原理 7
2.1 關節活動範圍(R.O.M) 7
2.2 人體解剖平面 7
2.3 方向估計演算法(Orientation Estimation Algorithm)[22] 8
2.3.1 四元數(Quaternion) 8
2.3.2 方向估計演算法架構 10
第三章、 研究方法 12
3.1 系統架構 12
3.2 硬體架構 13
3.3 韌體設計 15
3.4 藍牙傳輸封包格式 16
3.5 復健動作判斷 16
3.6 復健目標設定 17
3.7 復健參數設定 18
3.8 系統評分標準設定 19
3.9 手機軟體開發 20
3.10 居家端應用程式功能介紹 20
3.10.1 開始復健 21
3.10.2 上傳復健檔案 25
3.10.3 下載復健參數 26
3.10.4 裝置設定 27
3.11 實驗設計 28
3.11.1 本系統裝置準確度校正 28
3.11.2 光學三維影像系統和本系統裝置同時與馬達驗證 29
3.11.3 臨床實驗 30
3.11.4 系統問卷評價 33
第四章、 實驗結果 34
4.1 本系統裝置準確度校正 34
4.2 光學三維影像系統和本系統裝置同時與馬達驗證 37
4.3 臨床實驗 38
4.3.1 肩膀屈曲(Shoulder flexion) 39
4.3.2 肩膀伸直(Shoulder extension) 42
4.3.3 肩膀外展(Shoulder Abduction) 45
4.3.4 手肘屈曲(Elbow flexion) 48
4.3.5 結果整理與討論 51
4.4 問卷評價整理 52
第五章、 結論與未來展望 54
第六章、 參考文獻 56
附錄 60

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