除了傳統手工具及手套等保護配件，復健機器手臂、智能假肢等貼合於手部的新科技更需要精確的尺寸資訊，傳統接觸式量測不僅耗時費力，且容易產生誤差，因此發展一套快速且精確的尺寸擷取系統是有其必要性的。
本研究目的為建構一套手部特徵萃取與尺寸量測系統，透過三維掃描點雲資料根據其幾何外型和解剖學上之定義識別出特徵點，再以特徵點為基準擷取各項需要尺寸。掃描前分別在雙手黏貼7個標記點（手腕摺痕中點、中指與手掌掌側交接處中點、拇指近端指骨與掌骨背側交接處最凸點、食指近端指骨與掌骨背側交接處最凸點、中指近端指骨與掌骨背側交接處最凸點、無名指近端指骨與掌骨背側交接處最凸點、小指近端指骨與掌骨背側交接處最凸點），利用三維頭型掃描儀蒐集手部點雲數據，匯入手部點雲與預貼標記點座標後，根據標記點定義出縱軸方向，由此得到手腕切面，利用輪廓分析在手部冠狀切面搜尋手指分割基準點，藉由分割平面將手掌分割為手腕、掌面及五根手指七個區域，使用最小凸包與局部極值法於各區域分別獨立搜尋特徵點，並依據特徵點計算尺寸。
本系統共可識別出單手65個手部特徵點（含7個預貼標記點及58個搜尋出的特徵點）與73項手部尺寸，包含40項長度（含19項痕指節長度與19項指骨長度）、16項寬度、2項厚度及15項圍度。為驗證系統的可行性，招募100名（50男50女）20至26歲之研究參與者，透過三維掃描量測及手動量測分別蒐集上述尺寸。驗證結果顯示，在精確度部分，掃描量測之精確度平均絕對離差（MADPrecision）皆小於手動量測之精確度平均絕對離差（MADPrecision），說明掃描量測之再現性較佳；準確度部分，除手指根部圍度外，其餘尺寸之準確度平均絕對離差（MADAccuracy）皆在ISO 20685建議的容許誤差內。整體而言，本研究所建構之系統可替代傳統手動量測，快速精確地獲得手部尺寸。

In addition to traditional hand tools and protective accessories like gloves, new types of hand wearable devices, such as rehabilitation exoskeleton robots and intelligent prosthesis, require more accurate dimension information. Traditional contact measurement is not only time-consuming but also prone to errors. So it is necessary to develop an efficient, accurate and precise dimension extraction system.
This study aims to construct a hand feature extraction and dimension measurement system, which identifies the feature points through the geometric appearance and anatomical definition of three-dimensional point cloud data, and calculates the dimension based on the feature points. Before scanning, 7 landmarks are placed on each hand, including the midpoint of the wrist crease (WP), the point on the palmar side of the base of the middle finger (MBP), the point on the dorsal side of the metacarpophalangeal joints of the thumb (TMD), the point on the dorsal side of the metacarpophalangeal joints of the index finger (IMD), the point on the dorsal side of the metacarpophalangeal joints of the middle finger (MMD), the point on the dorsal side of the metacarpophalangeal joints of the ring finger (RMD) and the point on the dorsal side of the metacarpophalangeal joints of the little finger (LMD). After collecting hand point cloud data using the 3D head scanner and importing the coordinates, the vertical axis and the wrist section are obtained through referring WP and MBP. Subsequently, silhouette analysis is applied at coronal section to search for the finger root section. After that, each hand is segmented into 7 parts, including the wrist, the palm and the five fingers. Finally, convex hull and local extremum circumference determination are performed to identify feature points in each part respectively and calculate the dimensions through the identified feature points.
The system can help to identify 65 hand feature points (including 7 pre-marked landmarks and 58 feature points) and to obtain 73 hand dimensions, including 40 lengths (including 19 crease phalanges and 19 phalanges), 16 breadths, 2 thickness, and 15 circumferences. To verify the feasibility of the system, 50 males and 50 females (aged between 20 and 26 years old) were recruited in this study. Hand dimensions above were collected through 3D scanning and manual measurement methods. The results showed that the MADPrecision of 3D scanning is smaller than that of manual measurement, indicating the reproducibility of 3D scanning is better. Except the circumference of the finger roots, the MADAccuracy of all dimensions are within the tolerance error (1 mm). Consequently, the system may replace the traditional contact method for obtaining hand measurements efficiently and accurately.

摘要 I
Abstract II
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機 2
1.3 研究目的 2
1.4 研究架構 3
第二章 文獻探討 5
2.1 手部骨骼構造 5
2.2 非接觸式手部計測方法 7
2.2.1 影像量測 7
2.2.2 三維幾何量測 9
2.2.2.1 三維雷射掃描 9
2.2.2.2 樣板變型 10
2.2.3 小結 11
2.3 標記點 13
2.3.1 編碼 13
2.3.2 標記點位置選定 15
2.4 掃描姿勢 15
2.5 三維點雲辨識常用演算法 16
2.5.1 輪廓提取 16
2.5.2 最小凸包（convex hull） 18
第三章 系統建構 21
3.1 基本設定 21
3.1.1 特徵點編碼方法 21
3.1.2 手部座標定義 22
3.2 點雲資料蒐集 24
3.2.1 預貼標記點 24
3.2.2 掃描儀器與設備 25
3.2.3 掃描姿勢 25
3.3 系統架構 26
3.3.1 資料匯入與預處理 28
3.3.2 手掌分割 28
3.3.2.1 手掌輪廓搜尋 28
3.3.2.2 手指根部特徵點搜尋 29
3.3.2.3 手指分割 32
3.3.3 手腕特徵搜尋 32
3.3.4 手指特徵搜尋 33
3.3.4.1 指尖 33
3.3.4.2 指節 34
3.3.5 掌部特徵搜尋 35
3.3.6 尺寸計算 35
3.4 系統結果輸出 36
第四章 精確度與準確度驗證 47
4.1 研究參與者 47
4.2 實驗儀器與設備 48
4.3 量測項目與量測姿勢 48
4.4 實驗流程 48
4.5 驗證指標 51
4.6 精確度與準確度驗證結果 52
4.6.1 精確度驗證結果 52
4.6.2 準確度驗證結果 59
第五章 討論 66
5.1 手部尺寸定義問題 66
5.1.1 長度 66
5.1.2 寬度與圍度 67
5.2 三維掃描手部計測比較 69
5.2.1 量測方法比較 69
5.2.2 精確度與準確度比較 71
第六章 結論與建議 75
6.1 結論 75
6.2 建議 76
參考文獻 78

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