臺灣博碩士論文加值系統

多尺度熵（MSE）是一種近年來熱門的分析工具，用來代表多個尺度的時間序列複雜性，但較少利用此工具對不同輸入裝置的目標導向作業進行探討。本研究先回顧過去熵的概念、反覆運動及費茲定律相關研究，將實驗環境建立在費茲定律的框架下，進行顯示器環境設計、任務難度的定義及角度的設定，而實驗將分為兩種，實驗一為基礎的重複費茲反覆運動的目標導向作業，實驗二為包含角度因素的二維雙變量目標導向作業，透過動作捕捉系統（OptiTrack Motion Capture System）可將運動動作轉換成數位資料的特性，收集手部的運動軌跡，作為MSE分析的原始數據，以探討不同因子對於MSE的影響。其結果顯示，操控觸控筆的運動軌跡比操控滑鼠更具有複雜性；當任務需求增加時，其運動軌跡的複雜度會降低；當角度在大尺度時證實120-300度的運動軌跡比0-180度更複雜。期望透過本研究結果能為MSE應用於費茲反覆運動的作業上帶來貢獻。

Multiscale Entropy (MSE) is a popular tool for analysis to represent time series complexity at multiple scales in recent years. However, in MSE analysis there is a lack of relevant research on goal-directed tasks with input devices. In our study we review the relevant research in the concept of entropy, the reciprocal movement and the Fitts’ Law. Thus, the experimental environment is built under the framework of Fitts’ Law to design the display environment and define task difficulty and angle. The experiment is divided into two part. Experiment 1 is a basic repeated goal-directed task with reciprocal movement. Experiment 2 which considers the angle factor is a two-dimensional bivariate goal-directed task with reciprocal movement. By the OptiTrack Motion Capture System which can convert human characteristics into data, the trajectory of hand movement is collected for analysis to discuss the influence of different factors on the MSE. The results of the study were that the movement trajectory of using the stylus is more complex than mouse. When the task demand increases, the complexity of the movement trajectory will decrease. For angle factor, at large scale, it is confirmed that movement trajectory in 120-300 degree is more complex than 0-180 degrees. In the end, it is expected that the results of this study will contribute to the application of MSE.

致謝 I
摘要 II
ABSTRACT III
目錄 IV
圖目錄 VI
表目錄 VII
第一章 緒論 1
1.1背景與動機 1
1.2研究目的 2
第二章 文獻探討 4
2.1熵(ENTROPY)原理及複雜度 4
2.2費茲定理FITTS’ LAW 6
2.3輸入裝置相關研究 7
2.4反覆運動 8
第三章 研究方法 10
3.1受試者 10
3.2實驗設備 11
3.2.1光學式動作捕捉系統（OptiTrack Motion Capture System） 12
3.3實驗設計 13
3.3.1顯示器環境設計 13
3.3.2自變數 14
3.3.3應變數 16
3.3.4實驗任務 17
3.4實驗流程 17
3.5數據分析方法 20
3.5.1資料前處理 20
3.5.2多尺度樣本熵(Multiscale Entropy, MSE) 20
3.5.3統計分析 23
第四章 研究結果 24
4.1實驗一: 移動時間(MOVEMENT TIME) 24
4.1.1線性關係 24
4.1.2平均移動時間的差異 26
4.2實驗一:多尺度熵分析 29
4.2.1輸入裝置 29
4.2.2任務難度 30
4.2.3重複次數 31
4.2.4 SampEn的差異 33
4.3實驗二: 移動時間(MOVEMENT TIME) 37
4.3.1線性關係 37
4.3.2平均移動時間的差異 37
4.4實驗二:多尺度熵分析 40
4.4.1輸入裝置 40
4.4.2任務難度 41
4.4.3角度 42
4.2.4 SampEn的差異 44
第五章 討論 48
第六章 結論 51
6.1研究限制 51
6.2未來方向 52
參考來源 53
附錄 57

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