Human Leg Motion Tracking by IMU and single Camera Data Fusion using Extended Kalman Filter

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Taheri, Omid | 2017

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Type of Document: M.Sc. Thesis
Language: Farsi
Document No: 49273 (08)
University: Sharif University of Technology
Department: Mechanical Engineering
Advisor(s): Alasty, Aria; Salarieh, Hassan
Abstract:
Human motion capture is frequently used to study rehabilitation and clinical problems, as well as to provide realistic animation for the entertainment industry. IMU based systems as well as Marker based motion tracking systems are of most popular methods to track movement due to their low cost of implementation and lightweight. Results of IMU leads to unacceptable drift errors while marker based systems are Drift-free. Also, unlike cameras, IMUs are suitable for long-distance motion capturing. Based on the complementary properties of marker based systems and the inertial sensors, in this work, an Extended Kalman Filter approach was developed to fuse the data of two IMUs and a single camera for human leg motion tracking. In marker based system, depth of joints are also estimated using a mathematical model. The efficiency of the proposed algorithm was evaluated by an optical motion tracker system. The errors were reduced by almost 30 percent compared to cases when either inertial sensor or camera measurements were utilized
Keywords:
Motion Capture ; Data Fusion ; Inertial Sensor ; Optimization ; Depth Estimation ; Rehabilitation ; Extended Kalman Filter ; Marker Tracking

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