색확장형 깊이 인식 기반 인체 모션 트레킹 시스템

Abstract

학위논문 (박사)-- 서울대학교 대학원 : 기계항공공학부, 2016. 2. 이건우.Motion tracking system using depth sensing camera has been widely used in motion game, sports, rehabilitation, and so on, which has limita- tions of self-occlusion, full body tracking, and person-occlusion in med- ical applications such as virtual rehabilitation. First, in order to under- stand limitations of conventional vision based method, we developed several rehabilitation and assessment programs using depth sensing cam- era. Although we confirmed the treatment effect and similarity with real assessment by the clinical testing, the occlusion and system integration problems could not cover a lot of movement of human. In this study, we examine this limitation in the developing process of several rehabilita- tion application, and resolve this problem by implementing a new track- ing method called Color Augmented Depth Sensing (CA-DT). The color based segmentation, Principle Component Analysis (PCA), and error weighted interpolation were used to develop CA-DT. Consequently, CA- DT system allowed the users to move freely without problems by partial occlusion, connection with other person. The result of comparison with a motion capture system of the passive marker type shows 0.97 correla- tion coefficient, which means that tracking ability of CA-DT is similar to conventional system. Furthermore, we considered patch size and shortened the armband type up to 5 cm breadth.Chapter 1 Introduction 1 1.1. Overview 1 1.1.1. Stroke and Neurorehabilitation 1 1.1.2. Virtual Rehabilitation 6 1.1.3. Motion Tracking System 9 1.2. Motivation and Objectives 11 1.2.1. Issue of the Stroke 11 1.2.2. Studies for Virtual Rehabilitation 16 1.2.3. Depth Sensing based Skeleton Tracking 19 1.3. Our Approach 21 1.3.1. Virtual Rehabilitation and Assessment 22 1.3.2. Color Augmented Depth Tracking 24 Chapter 2 Virtual Rehabilitation and Assessment Programs 27 2.1. Overview 27 2.2. Intensive Therapy Programs . 29 2.2.1. Introduction 29 2.2.2. Result 32 2.3. Virtual Box and Block Test [37] 39 2.3.1. Introduction 39 2.3.2. System Design and Algorithm Development 41 2.3.3. Result and Discussion 48 2.4. Virtual Fugl Meyer Assessment 52 2.4.1. Introduction 52 2.4.2. Data Acquisition 53 2.4.3. Feature Extraction 56 2.4.4. Learning Model for FMA score Prediction 60 2.4.5. Result and Discussion 61 Chapter 3 Color Augmented Depth Tracking Method 66 3.1. Introduction 66 3.2. System Overview 72 3.3. Cloth Design 76 3.4. Body Segmentation Algorithm 79 3.4.1. Introduction 79 3.4.2. Algorithm Development 81 3.4.3.Result 86 3.5. Body Axis Tracking Algorithm 90 3.5.1. Introduction 90 3.5.2. Cylindrical Fitting on 3D Point Cloud 94 3.5.3. Fitting Error Weighted Interpolation Method 98 3.5.4. Result 101 3.6. Hand Tracking Algorithm 104 3.6.1. Introduction 104 3.6.2. Hand Detection with Full Body Tracking 105 3.6.3. Result 106 3.7. Body Part Connection 109 3.8. Discussion 111 Chapter 4 Evaluation of CA-DT System 112 4.1. Introduction 112 4.2. Skeleton Tracking Performance 116 4.2.1. Self-Occlusion 116 4.2.2. Connection with Others 117 4.2.3. Hand Tracking 118 4.3. Biomedical Validation 119 4.3.1. Design and Procedure 119 4.3.2. Result and Discussion 121 4.4. Patch Length Specification 125 4.4.1. Introduction 125 4.4.2. Simulation as Cylindrical Length 126 4.4.3. Result and Discussion 129 Chapter 5 Conclusion 132 Bibliography 134 초록 148Docto