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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 王凡 | zh_TW |
| dc.contributor.advisor | Farn Wang | en |
| dc.contributor.author | 王瀚緯 | zh_TW |
| dc.contributor.author | Han-Wei Wang | en |
| dc.date.accessioned | 2025-02-21T16:20:52Z | - |
| dc.date.available | 2025-12-10 | - |
| dc.date.copyright | 2025-02-21 | - |
| dc.date.issued | 2024 | - |
| dc.date.submitted | 2024-12-17 | - |
| dc.identifier.citation | Hanwei Wang, Feipei Lai and Farn Wang, "Real-Time Multiple Human Height Measurements With Occlusion Handling Using LiDAR and Camera of a Mobile Device," in IEEE Access, vol. 12, pp. 122588-122596, 2024, doi: 10.1109/ACCESS.2024.3447153.
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/96742 | - |
| dc.description.abstract | 醫學測量在診斷、監測和治療各種疾病和健康問題方面扮演著重要角色，醫療環境需要醫護人員在短時間內做出迅速而準確的決策和行動，因此提供即時精準的醫學測量是十分重要的，然而現今所使用的一般傳統醫學測量方式，還需要許多人力介入判斷，不僅耗時費工，也缺乏統一的測量標準，我們必須尋找更快速精準且統一的辦法，以確保達到更好的醫療護理並改善醫療時間。
這篇論文提出一套架構，利用手持裝置的光學雷達與相機取得醫療待測物在真實世界的三維座標，加入機器學習方法的判斷輔助，達到非接觸式且即時的精準醫學測量，改善了一般傳統醫學測量利用人力判斷耗時費工的問題，且讓醫學測量有了統一測量標準，因為是由機器自動判斷，同樣的測量圖片不會有不同人判斷得到不同結果的問題。 本論文基於此架構提出了多項即時醫學測量的應用，例如：測量身高 [1]、測量褥瘡面積 [2]、測量燙傷面積 [3]以及跌倒偵測 [4]，並在手持裝置上開發了多套醫學測量軟體來驗證此架構。醫療人員及一般民眾皆可以利用具備光學雷達與相機的手持裝置（例如iPhone Pro與iPad Pro），在下載安裝我們開發的軟體後即可做即時的醫學測量。手持裝置是很適合做為醫學測量的工具，因為手持裝置隨手可及的便利性以及無遠弗屆的傳輸能力，讓遠距醫療測量能真正普及在一般大眾的生活之中，不再是遙不可及的夢想。 | zh_TW |
| dc.description.abstract | Medical measurements play a crucial role in diagnosing, monitoring, and treating various diseases and health problems. Quick and accurate decisions are essential in the medical environment, which emphasizes the need for immediate and precise medical measurements. However, current traditional medical measurement methods rely heavily on human intervention and judgment, leading to time-consuming and labor-intensive processes that lack standardized measurement standards. Enhancing medical care requires faster, more accurate, and unified methods to optimize the time spent on medical procedures.
This paper introduces an architecture that utilizes the LiDAR and camera of a mobile device to capture real-world three-dimensional coordinates of medical objects for measurement. It also incorporates machine learning methods to make precise, non-contact medical measurements in real-time. This approach aims to reduce reliance on human judgment, streamlining medical measurements and establishing unified standards to eliminate discrepancies caused by different individuals making assessments. Building upon this architecture, the paper proposes several medical measurement applications, including Height Measurement [1], Bedsore Area Measurement [2], Burn Area Measurement [3], and Fall Detection [4]. Additionally, we develop sets of medical measurement software for mobile devices to validate this architecture. The applications enable medical staff and the general public to perform real-time medical measurements using mobile devices equipped with LiDAR and cameras (for example, iPhone Pro and iPad Pro), after downloading and installing our software. Utilizing mobile devices for medical measurements is highly advantageous due to their convenience and widespread accessibility, making telemedicine measurement more feasible for the general public. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2025-02-21T16:20:52Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2025-02-21T16:20:52Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 誌謝 I
中文摘要及關鍵詞 II ABSTRACT AND KEYWORDS III CONTENTS IV LIST OF FIGURES VI LIST OF TABLES VII CHAPTER 1 INTRODUCTION 1 1.1 MEDICAL MEASUREMENT SYSTEM UTILIZING LIDAR, CAMERA, AND MACHINE LEARNING ON MOBILE DEVICES 1 1.1.1 Advantage of Medical Measurement Using LiDAR and Camera 2 1.1.2 Advantage of Finding Medical Objects Using Machine Learning Methods 3 1.1.3 Advantage of Medical Measurement Using Mobile Device 4 1.2 PROCESS OF OBTAINING THE 3D COORDINATES OF THE MEDICAL OBJECTS 5 1.2.1 Conversion of Coordinates in the Real-World 6 1.2.2 Medical Object Detection Methods 8 1.3 STRUCTURE OF THE DOCTORAL DISSERTATION 8 CHAPTER 2 MEASURE BODY HEIGHT 11 2.1 INTRODUCTION 11 2.2 METHODS 14 2.2.1 Acquire the 3D Coordinates of the Human Faces 16 2.2.2 Determine the Heights of the Ground and the Eye 17 2.2.3 Body Height Calculation 17 2.3 EXPERIMENTAL RESULTS 18 2.3.1 “Measuring Body Height” Mobile Application 18 2.3.2 Precision of Measuring Body Height 19 2.3.3 Accuracy of Multiple Body Heights Measured in One Shot 20 2.3.4 Accuracy of Multiple Body Heights Measured Wearing Glasses and Shoes in One Shot 21 2.3.5 Accuracy of Body Height Measurement When Part of the Body Is Obscured 23 2.3.6 Accuracy of Body Height Measurement at Different Camera Heights and Angles 24 2.3.7 Variable Brightness of the Scene 25 2.4 DISCUSSION 26 2.4.1 Comparison with Prior Work 27 2.4.2 Limitations 29 2.5 CONCLUSION 30 CHAPTER 3 MEASURE BEDSORE AREA 32 3.1 INTRODUCTION 32 3.2 METHODS 34 3.2.1 Annotating Data for Machine Learning Models 34 3.2.2 Segmentation Using U-Net 35 3.2.3 Segmentation Using Mask R-CNN 35 3.2.4 “Pressure Ulcer Measure” Mobile Application 36 3.2.5 Bedsore Area Calculation 37 3.2.6 Validation of Bedsore Area Measurement 39 3.3 EXPERIMENTAL RESULTS 40 3.3.1 Accuracy of Bedsore Area Measurement 40 3.4 DISCUSSION 42 3.4.1 Comparison with Prior Work 42 3.4.2 Limitations 46 3.5 CONCLUSION 46 CHAPTER 4 MEASURE BURN AREA 48 4.1 INTRODUCTION 48 4.2 METHODS 50 4.2.1 Annotating Data for Machine Learning Models 50 4.2.2 “Burn Evaluation Network” Mobile Application 51 4.2.3 Burn Area Measurement 52 4.3 EXPERIMENTAL RESULTS 56 4.3.1 Accuracy of Burn Wound Area Measurement 56 4.3.2 Experimental Result of Real Patients 58 4.4 DISCUSSION 64 4.4.1 3D Photography 65 4.4.2 3D Measurements 66 4.4.3 3D Burn Area Measurement 67 4.4.4 Comparison with Prior Work 68 4.4.5 Limitations 68 4.5 CONCLUSION 69 CHAPTER 5 FALL DETECTION 70 5.1 INTRODUCTION 71 5.2 METHODS 73 5.2.1 Acquire the 3D Coordinates of the Human Skeleton 74 5.2.2 Determine the Heights of the Ground and the Head 75 5.2.3 Fall Judgment 76 5.2.4 “Fallert” Mobile Application 78 5.3 EXPERIMENTAL RESULTS 79 5.3.1 Precision in Determining Whether the Head is Close to the Ground 79 5.3.2 Accuracy of Six Types of Fall Activities Detection 81 5.3.3 Distinguish Falls on The Bed/Sofa, Sit-ups, and Falls on The Ground 83 5.3.4 Dynamic Evaluation of Yoga Posture 84 5.3.5 Variable Brightness of the Scene 86 5.4 DISCUSSION 87 5.4.1 Limitations 89 5.4.2 Comparison with Prior Work 90 5.4.3 Future Work 91 5.5 CONCLUSION 91 CHAPTER 6 CONCLUSION 93 6.1 FUTURE WORK 93 6.2 PRELIMINARY APPLICATIONS FOR AUTOMATIC MEASUREMENT SYSTEM 94 6.3 CONCLUSION 95 REFERENCE 97 | - |
| dc.language.iso | en | - |
| dc.subject | 應用軟體 | zh_TW |
| dc.subject | 機器學習 | zh_TW |
| dc.subject | 相機 | zh_TW |
| dc.subject | 光學雷達 | zh_TW |
| dc.subject | 手持裝置 | zh_TW |
| dc.subject | 醫學測量 | zh_TW |
| dc.subject | Mobile Device | en |
| dc.subject | Medical Measurement | en |
| dc.subject | Applications | en |
| dc.subject | LiDAR | en |
| dc.subject | Camera | en |
| dc.subject | Machine Learning | en |
| dc.title | 利用手持裝置上的光學雷達與相機透過機器學習方法在醫學測量的各種應用 | zh_TW |
| dc.title | Medical Measurement Applications Utilizing LiDAR and Camera with Machine Learning Methods on a Mobile Device | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 113-1 | - |
| dc.description.degree | 博士 | - |
| dc.contributor.oralexamcommittee | 賴飛羆;林澤;李鴻璋;陳沛甫 | zh_TW |
| dc.contributor.oralexamcommittee | Feipei Lai;Che Lin;Hung-Chang Lee;Pei-Fu Chen | en |
| dc.subject.keyword | 醫學測量,應用軟體,光學雷達,相機,機器學習,手持裝置, | zh_TW |
| dc.subject.keyword | Medical Measurement,Applications,LiDAR,Camera,Machine Learning,Mobile Device, | en |
| dc.relation.page | 113 | - |
| dc.identifier.doi | 10.6342/NTU202404700 | - |
| dc.rights.note | 同意授權(限校園內公開) | - |
| dc.date.accepted | 2024-12-17 | - |
| dc.contributor.author-college | 電機資訊學院 | - |
| dc.contributor.author-dept | 電機工程學系 | - |
| dc.date.embargo-lift | 2025-12-10 | - |
| 顯示於系所單位： | 電機工程學系 | |
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