先進的資訊科技已逐步實現了企業中數位設計流程的願景，而在先期設計階段中引進數位人體模型的應用也已成為流程中重要的設計活動。然而如何快速的產生合理且具有實用價值的人體資訊以協助進行設計上的考量，一直是採納此技術的關鍵。因此，本研究著重於發展具實用價值的人體動作產生方法，所發展出的方法包括有即時性的動作產生技術以及非即時性的動作控制機制。
首先提出的「互動式虛擬評估法」為一即時性的動作產生方法與人因評估流程。將虛擬實境、動作擷取與數位人體模型三大技術整合。設計師與工程師可以在虛擬實境裡快速的建造產品與系統介面的虛擬原型，使用者能融入虛擬實境與其進行互動，並藉由數位人體模型上發展的評估方法來對設計概念進行測試評估，以進行改善。此方法被應用於一飛彈發射車內控制介面的設計評估。接著發展出的「MTM動作產生機制」為一非即時性的標準語意控制方法，目的在於建立自動且標準化的動作產生流程與系統。此方法採用業界標準的Method Time Measurement (MTM) 此作業時間規範來做為動作語意的輸入基礎，並藉由預設的動作產生機制，來產生標準化的作業導向人體模擬。「自然語言動作產生機制」則為上一研究成果的延續，藉由將輸入模式改變為直覺的自然語言、並增加動作產生機制的功能、引進車廠實證研究得到的時間資料庫，來加強語意產生動作系統的準確性與實用價值，此方法已建立於一PLM系統之中，並應用於車廠中來進行工作與工作站的設計評估。
本研究所提出的人體動作產生機制與系統，可協助企業進行從產品設計、介面設計到工作與工作站設計的先期人因工程評估。

The digital design process is progressively realized by advanced information technology nowadays. Using digital human model (DHM) in the early phase of design has become a vital practice in industry. However, how to simulate the realistic human motion, to facilitate the motion generating process, and to produce valuable human information are always the main concerns in this field. Therefore, this research focused on developing the practicable motion generating methods including the real-time interactive method and the semantic methods.
First of all, the Interactive Virtual Evaluation (IVE) method was proposed in this research. By integrating the VR technology, motion capture, and digital human model (DHM) system, the virtual prototypes of a system and interface design can be created and tested in the digital world. The proposed method has been applied to the controller interface design of a missile launching vehicle.
Further, the semantic method, Motion Generation from MTM, was to automate and standardize the motion creation process. A time study method known as “Method Time Measurement (MTM)” was used as the basis for defining the operational motion semantics which can be used by the designer to produce the human simulation in the digital environment. Motion Generation from Natural Language was the subsequent research of the MTM method. Through changing the input from MTM semantics to natural language, increasing the functions in motion generation algorithm, and introducing empirical time database accumulated from automotive industry, the semantic method was able to enhance in its accuracy and practical value. These semantic methods have been applied to automotive industry for work and workplace design.

中文摘要 1
ABSTRACT 2
致謝 3
TABLE OF CONTENTS 4
FIGURE LIST 6
CHAPTER 1 INTRODUCTION 9
1.1 THE BACKGROUND OF THE RESEARCH 9
1.2 THE PURPOSE OF THIS STUDY 10
1.3 THE FRAMEWORK OF THIS DISSERTATION 11
CHAPTER 2 DIGITAL HUMAN IN DESIGN PROCESS 12
2.1 VIRTUAL PROTOTYPING IN DESIGN PROCESS 12
2.2 DIGITAL HUMAN MODELING IN DESIGN PROCESS 14
2.2.1 The ergonomics aspect in digital design process 16
2.2.2 The efficiency aspect in digital design process 17
CHAPTER 3 HUMAN MOTION CONTROL 20
3.1 VIRTUAL REALITY 20
3.2 INVERSE KINEMATICS 21
3.3 MOTION CAPTURE 21
3.4 MOTION EDITING 21
3.5 MOTION GENERATION FROM SEMANTICS 22
CHAPTER 4 INTERACTIVE VIRTUAL EVALUATION 24
4.1 VIRTUAL EVALUATION 24
4.1.1 IK-based DHM 26
4.1.2 Virtual prototyping: human-product interaction kit 27
4.1.3 Virtual testing 30
4.1.4 DHM simulation 32
4.1.5 Design Reviewing 34
4.2 IMPLEMENTATION 34
4.3 DISCUSSION 36
CHAPTER 5 MOTION GENERATION FROM MTM SEMANTICS 39
5.1 MOTION GENERATION 39
5.1.1 Motion editing tool 40
5.1.2 Motion generation from MTM semantics 43
5.2 IMPLEMENTATION 48
5.2.1 System architecture 48
5.2.2 Interface tier 48
5.2.3 Function tier 49
5.2.4 Data tier 51
5.2.5 The scenario of evaluating work and workplace design 52
5.3 DISCUSSION 53
CHAPTER 6 MOTION GENERATION FROM NATURAL LANGUAGE 55
6.1 MOTION GENERATION FROM NATURAL LANGUAGE 55
6.1.1 Virtual prototyping 56
6.1.2 Natural language instruction 59
6.1.3 MTM translator 61
6.1.4 Motion generator 61
6.2 IMPLEMENTATION 64
6.2.1 Example of generating motion from MGS program 64
6.2.2 System validation 68
6.3 DISCUSSION 70
CHAPTER 7 CONCLUSIONS 71
7.1 INTERACTIVE VIRTUAL EVALUATION (IVE) 71
7.2 MOTION GENERATION FROM MTM SEMANTICS 72
7.3 MOTION GENERATION FROM NATURAL LANGUAGE 72
7.4 FURTHER RESEARCHES 73
REFERENCES 75

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