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作者(中文):陳品捷
作者(外文):Chen, Pin-Chieh
論文名稱(中文):以手部三維掃描為基礎的特徵擷取與尺寸計算方法
論文名稱(外文):Hand Feature Extraction and Dimension Measurement Using 3D Scanning Data
指導教授(中文):王茂駿
盧俊銘
指導教授(外文):Wang, Mao-Jiun
Lu, Jun-Ming
口試委員(中文):石裕川
邱敏綺
口試委員(外文):Shih, Yuh-Chuan
Chiu, Min-Chi
學位類別:碩士
校院名稱:國立清華大學
系所名稱:工業工程與工程管理學系
學號:104034527
出版年(民國):106
畢業學年度:105
語文別:中文
論文頁數:83
中文關鍵詞:手部計測三維掃描特徵辨識尺寸擷取
外文關鍵詞:hand anthropometry3D scanningfeature extractiondimension extraction
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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
1. 王茂駿、王明揚、林昱呈(2001)。台灣地區人體計測資料庫手冊,中華民國人因工程學會。
2. 陳濤、李光耀(2004)。平面離散點集的邊界搜索算法,計算機仿真,21(3),21-23。
3. 黃柏盛(2007)。手部靜態尺寸與功能性指寸之對應關係-以抓握為例,清華大學工業工程與工程管理學系碩士論文。
4. 演算法筆記- Convex Hull(2013)。演算法筆記。取自http://www.csie.ntnu.edu.tw/~u91029/ConvexHull.html#5。
5. 潘映辰(2014)。利用RGB-D感測器與統計學習模型即時估算人體計測值,清華大學工業工程與工程管理學系碩士論文。
6. 蔡舒雅(2015)。人體掃描系統技術開發,醒吾科技大學資訊科技應用系碩士論文。
7. 鄭秋隆(2005)。自動化手部特徵萃取與手部模型之建立,成功大學機械工程學系碩士論文。
8. 龔慧怡(2016)。手部靜態與動態計測資料庫及尺碼系統之建構,清華大學工業工程與工程管理學系碩士論文。
9. Andrew, A.M. (1979). Another Efficient Algorithm for Convex Hulls in Two Dimensions. Information Processing Letters, 9, 216-219.
10. Barber, C.B., Dobkin, D.P., & Huhdanpaa, H. (1996). The quickhull algorithm for convex hulls. ACM Transactions on Mathematical Software (TOMS), 22(4), 469-483.
11. Barron, C., & Kakadiaris, I.A. (2000). Estimating anthropometry and pose from a single image. Proceedings of the 2000 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1, 669-676.
12. Barter, J.T., & Alexander, M. (1956). A sizing system for high altitude gloves. Wright Air Development Center, Wright-Patterson Air Force Base, Ohio.
13. Boisvert, J., Shu, C., Wuhrer, S., & Xi, P. (2013). Three-dimensional human shape inference from silhouettes: reconstruction and validation. Machine vision and applications, 24(1), 145-157.
14. Cakit, E., Durgun, B., Cetik, O., & Yoldas, O. (2014). A survey of hand anthropometry and biomechanical measurements of dentistry students in Turkey. Human Factors and Ergonomics in Manufacturing & Service Industries, 24(6), 739-753.
15. Chan, T.M. (1996). Optimal output-sensitive convex hull algorithms in two and three dimensions. Discrete and Computational Geometry, 16, 361–368.
16. Chandra, A., Chandna, P., & Deswal, S. (2011). Analysis of hand anthropometric dimensions of male industrial workers of Haryana state. International Journal of Engineering, 5(3), 242-256.
17. Courtney, A.J., & Ng, M.K. (1984). Hong Kong female hand dimensions and machine guarding. Ergonomics, 27(2), 187-193.
18. Davies, B.T., Abada, A., Benson, K., Courtney, A., & Minto, I. (1980). A comparison of hand anthropometry of females in three ethnic groups. Ergonomics, 23(2), 179-182.
19. Endo, Y., Tada, M., & Mochimaru, M. (2014). Hand modeling and motion reconstruction for individuals. International Journal of Automation Technology, 8(3), 376-387.
20. Garrett, J.W. (1970). Anthropometry of the air force female hand. AMRL-TR-69-26. Aerospace Medical Research Laboratory, Wright-Patterson AFB, Ohio.
21. Garrett, J.W. (1970). Anthropometry of the hands of male air force flight personnel. AMRL-TR-69-42. Aerospace Medical Research Laboratory, Wright-Patterson AFB, Ohio.
22. Garrett, J.W. (1971). The adult human hand: some anthropometric and biomechanical considerations. Human Factors: The Journal of the Human Factors and Ergonomics Society, 13(2), 117-131.
23. Ghosh, S.K., & Poirier, F. (1987). Photogrammetric technique applied to anthropometric study of hands. Journal of biomechanics, 20(7), 729-732.
24. Graham, R.L. (1972). An Efficient Algorithm for Determining the Convex Hull of a Finite Planar Set. Information Processing Letters, 1, 132-133.
25. Greiner, T.M. (1991). Hand anthropometry of U.S. Army Personnel. U.S. Army Natick research, Development and Engineering Center. Natick: MA. (NTIS No.ADA244533).
26. Heap, T., & Hogg, D. (1996). Towards 3D hand tracking using a deformable model. Proceedings of the Second International Conference on In Automatic Face and Gesture Recognition, 140-145.
27. International Organization for Standardization. (2005). ISO: 20685: 3D Scanning methodologies for internationally compatible anthropometric databases.
28. ISO 20685 (2005). “3D Scanning Methodologies for Internationally Compatible Anthropometric Databases”, in International Organization for Standardization. Reference no. 20685-2005. ISO, Switzerland.
29. ISO 7250 (1996). “Basic human body measurements for technological design”, in International Organization for Standardization. Reference no. 7250-1996. ISO, Switzerland.
30. Jarvis, R.A. (1973). On the identification of the convex hull of a finite set of points in the plane. Information Processing Letters, 2, 18–21.
31. Kouchi, M. (2012). AIST 日本人の手の寸法データ. Retrieved August, 16.
32. Lai, J., Wang, B., Fu, Q., & Wu, Z.Z. (2014). Automatic extraction method of human body sizes based on 3D point clouds. Journal of Central South University (Science and Technology), 45(8), 2676-2683.
33. Li, Z., Chang, C.C., Dempsey, P.G., Ouyang, L., & Duan, J. (2008). Validation of a three-dimensional hand scanning and dimension extraction method with dimension data. Ergonomics, 51(11), 1672-1692.
34. Lu, J.M., & Wang, M.J.J. (2008). Automated anthropometric data collection using 3D whole body scanners. Expert Systems with Applications, 35(1), 407-414.
35. Lu, J.M., & Wang, M.J.J. (2010). The evaluation of scan-derived anthropometric measurements. IEEE Transactions on Instrumentation and Measurement, 59(8), 2048-2054.
36. Magno, K.J.H., & Pabico, J.P. (2015). Towards input device satisfaction through hand anthropometry. Philippine Information Technology Journal, 4(1), 17-28.
37. Mandahawi, N., Imrhan, S., Al-Shobaki, S., & Sarder, B. (2008). Hand anthropometry survey for the Jordanian population. International Journal of Industrial Ergonomics, 38(11), 966-976.
38. Meunier, P., & Yin, S. (2000). Performance of a 2D image-based anthropometric measurement and clothing sizing system. Applied Ergonomics, 31(5), 445-451.
39. Nag, A., Nag, P.K., & Desai, H. (2003). Hand anthropometry of Indian women. Indian Journal of Medical Research, 117, 260.
40. Okunribido, O.O. (2000). A survey of hand anthropometry of female rural farm workers in Ibadan, Western Nigeria. Ergonomics, 43(2), 282-292.
41. OpenStax. (2014). Anatomy & Physiology. Retrieved from http://cnx.org/contents/14fb4ad7-39a1-4eee-ab6e-3ef2482e3e22@6.27.
42. Ran, L., Zhang, X., Chao, C., Liu, T., & Dong, T. (2009, July). Anthropometric Measurement of the Hands of Chinese Children. Lecture Notes in Computer Science, Digital Human Modeling, 5620, 46-54.
43. Vicinus, J.H. (1962). X-ray anthropometry of the hand. AMRL-TDR-62–111. Aerospace Medical Research Laboratories, Wright-Patterson AFB, Ohio.
44. Wibowo, R.K.K., & Soni, P. (2014). Anthropometry and agricultural hand tool design for Javanese and Madurese farmers in east Java, Indonesia. APCBEE Procedia, 8, 119-124.
45. Wintergreen Research (2015). Exoskeletons: Market Shares, Strategies, and Forecasts, Worldwide, 2015 to 2021(Report # SH26311852). Retrieved from http://wintergreenresearch.com/medical-exoskeletons
46. Zheng, X.H., Ding, S.T., Wu, Y.M., Xiao, H., Qi, J.C., & Niu, J.W. (2011, September). Dimension extraction from three dimensional (3D) hand data without prior manual landmarking. Proceedings of the 2011 IEEE 18th International Conference on Industrial Engineering and Engineering Management (IE&EM), Changchun, 1667-1670.
47. Zhou F. (2010). The Use of Alpha Shapes Contour Extraction of Discrete Points. Journal of HuBei TV University, 30(2), 155-156.
48. Zhu, H.H., Liu, Z.L., Zhai, Y.Q., & Lu, J. (2002). The Border Hunting Method at Measure Points in Waterway Dredging Area. Journal of WuHan University of Technology (Transportation Science & Engineering), 26(3), 309-311.
 
 
 
 
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