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研究生:郭曜寬
研究生(外文):Kuo Yau-Kuan
論文名稱:基於可穿戴式慣性傳感器運動捕捉的人體位移估計
論文名稱(外文):Human-Body Displacement Estimation Based on Wearable Inertial Sensors Motion Capture
指導教授:劉興民
指導教授(外文):Liu Shing-Min
口試委員:林文杰盧天麒
口試委員(外文):Lin Wen-ChiehLu Tain-Chi
口試日期:2021-01-22
學位類別:碩士
校院名稱:國立中正大學
系所名稱:資訊工程研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2021
畢業學年度:109
語文別:英文
論文頁數:106
中文關鍵詞:動態捕捉九軸慣性傳感器生物力學模型位移估計零速度修正
外文關鍵詞:motion capturenine-axis inertial measurement unitbiomechanical modeldisplacement estimationzero velocity update (ZUPT, ZVU)
相關次數:
  • 被引用被引用:0
  • 點閱點閱:253
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:1
近年來隨著物聯網(Internet of Things)、微型傳感器等技術的進步,體積較小、價格便宜、運動空間較不受限、無遮蔽問題的慣性傳感器動態捕捉系統則越來越受到歡迎。但由於慣性傳感器原始數據所估算的位移,會隨著誤差的累積越來越不精準。所以如何修正或抑制其誤差的累積,長時間的使用慣性傳感器成為了一個重要的課題。

我們提出一種基於可穿戴式慣性傳感器估計人體位移的動態捕捉方法,該算法透過配戴14個九軸慣性傳感器捕捉人體各部位的運動資訊,使用生物力學模型估算人體位移。並且我們設計了4個校正姿勢,以提升空間中定位的精準度。我們額外提出一種融合算法,將傳感器軌跡與模型直線距離進行結合,以彌補傳感器誤差累積所造成的最終位置誤差誤差以及受人體軟組織所造成的模型軌跡誤差,計算出更加準確的空間位移。最後本研究會同時讓使用者穿戴光學式系統與慣性傳感器系統進行動作,兩套系統分別估算出人體各個部位的位移,我們以精準度有一個數量級以上差距的光學式系統數據為標準,來驗證我們的方法能夠有效提升估算的位移準確度。
In recent years, with the advancement of technologies such as the Internet of Things and micro-sensors, inertial sensor motion capture systems that are small in size, inexpensive, unlimited movement space, and no occlusion problems have become more and more popular. However, the displacement estimated from the inertial sensors will become increasingly inaccurate as error accumulates. Therefore, how to correct or suppress the accumulation of errors, long-term use of inertial sensors has become an important issue.

We aim to develop an innovative motion capture algorithm for wearable inertial sensors. Our algorithm captures motion data through 14 nine-axis inertial sensors, and estimates the displacement of each key parts of human body based on biomechanical model. We also designed 4 calibration poses to improve the accuracy of spatial positioning. We additionally present a Biomechanical-Sensor (BS) hybrid algorithm that combines the sensor trajectory with the final position point of the biomechanical model to compensate for the displacement error of the final position point. Such error is caused by the accumulation of sensor errors along with the biomechanical model trajectory error caused by human soft tissue. In this study, the subjects wore the devices from optical system and inertial sensor system at the same time and performed motions. The two systems separately estimate the displacement of each part of the human body. Usvally the optical system data is an order of maqnitude more accurate than that of inertial sensor data. We use optical system data as the standard to verify that our algorithm can effectively improve the estimated displacement accuracy using inertial sensors.
Table of Contents
Abstract ii
中文摘要 iv
Acknowledgements vi
Table of Contents vii
List of Figures x
List of Tables xiv
Chapter 1: Introduction 1
1.1 Research Motivation and Objective 1
1.1.1 Optical-Based System 2
1.1.2 Non-optical system 2
1.2 Research Contribution 4
1.2.1 Estimating the displacement of body parts using biomechanical
model 4
1.2.2 Calibration pose 4
1.2.3 Biomechanical-Sensor (BS) hybrid algorithm 5
Chapter 2: Related Works 6
2.1 Inertial sensor displacement estimation 6
Chapter 3: Approach 15
3.1 Biomechanical Model 16
3.1.1 Body size measurement and model deformation 17
3.1.2 Orientation (Posture) of model parts 18
Magnetometer calibration 21
Reference vector 23
3.1.3 Displacement estimation of model hand 31
Zero velocity update (ZUPT, ZVU) 33
3.2 Calibration Pose 38
3.3 BS Hybrid Algorithm 43
Chapter 4: Experiments and Analysis 47
4.1 System Configuration 47
4.2 Experimental Methodology 52
4.3 Experimental Results 58
Chapter 5: Conclusion 85
References 87
Biography 91

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