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Sensor fusion for enhanced location tracking in virtual reality microsurgical environment

Title
Sensor fusion for enhanced location tracking in virtual reality microsurgical environment
Translated Title
가상현실 미세수술환경에서의 정밀위치측정을 위한 센서융합
Authors
John David Prieto Prada
DGIST Authors
Prieto, John; Song, Cheol; Choi, Ji Hwan
Advisor(s)
Cheol Song
Co-Advisor(s)
Jihwan Choi
Issue Date
2018
Available Date
2018-08-29
Degree Date
2018. 8
Type
Thesis
Keywords
Virtual reality, Sensor fusion, Kalman filter, Micro-surgery, Healthcare, 가상 현실, 센서 융합, 칼만 필터, 미세 수술, 건강 관리.
Abstract
For a delicate micromanipulation, subjects need to perform tasks while effectively minimizing their hand tremor. Recently, there is growing interest in creating virtual reality (VR) microsurgery systems to enable enhanced visual feedback for precise manipulation tasks as well as a training environment. For an accurate micro-location, it is needed the use of various sensors (sensor fusion) which encompasses different ways of combining data through algorithms in order to refine information quality or derive more information about it. To enable precise location tracking of surgical tools (e.g., forceps), we propose a VR microsurgical environment featuring a sensor fusion algorithm for location tracking system with an additional inertial measurement unit (IMU). The system reconstructs an image of surgical instruments (i.e., forceps) and 2D figures with a zoomed view of them, to help surgeons have a more precise sense in visual feedback. We hypothesize that merging two different sensors and matching them in a Kalman filter algorithm, the micro-localization will be more accurately. We develop and test two ways, that is, (1) location tracking without Kalman filter, and (2) location tracking with Kalman filter activated. A performance evaluation of location tracking in terms of error has been conducted in real and virtual experiments. The result shows that the condition with sensor fusion activated achieves more precise location tracking than without Kalman filter condition.
Table Of Contents
1. Introduction 1-- 1.1 Study Background 1-- 1.2 Previous Studies 2-- 1.2.1 Virtual reality applications 2-- 1.2.2 The importance of virtual reality 2-- 1.2.3 Visual stimuli in virtual reality to reduce hand tremor 3-- 1.3 Study Purpose 5-- 2. Sensor Fusion 6-- 2.1 Sensor Fusion Algorithms 6-- 2.1.1 Bayesian network 6-- 2.1.2 Dempster-Shafer 9-- 2.1.3 Convolutional neural networks 10-- 2.2 Sensor Fusion Methods 11-- 2.2.1 Smoothing 11-- 2.2.2 Filtering 12-- 2.2.3 Prediction 12-- 2.3 Sensor Fusion Example 12-- 2.4 Theory of Kalman Filter 14-- 2.5 System Model for Kalman Filter 18-- 3. Implementation of the VR System 18-- 3.1 System Architecture 18-- 3.2 Hardware System 19-- 3.3 Software System 19-- 3.4 General Schematic of the System 20-- 4. Experiments 21-- 4.1 Single Axis Dot by Dot 21-- 4.2 Motorized Trace 24-- 4.2.1 X axis 24-- 4.2.2 Y axis 26-- 4.3 2-D Axis Trace 27-- 4.3.1 2-D printed circle 28-- 4.3.2 VR circle 31-- 5. Discussion and Conclusion 35-- REFERENCES 36
URI
http://dgist.dcollection.net/common/orgView/200000102660
http://hdl.handle.net/20.500.11750/9193
DOI
10.22677/thesis.200000102660
Degree
Master
Department
Robotics Engineering
University
DGIST
Related Researcher
  • Author Song, Cheol Intelligent Bio-OptoMechatronics Lab
  • Research Interests Handheld medical robotics; Smart robotic microsurgery; Smart neuro-rehabilitation; Bio-photonic sensing and imaging
Files:
Collection:
Department of Robotics EngineeringThesesMaster


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