A Full-State Robust Extended Kalman Filter for Orientation Tracking During Long-Duration Dynamic Tasks Using Magnetic and Inertial Measurement Units

Accurate and robust orientation estimation using magnetic and inertial measurement units (MIMUs) has been a challenge for many years in long-duration measurements of joint angles and pedestrian dead-reckoning systems and has limited several real-world applications of MIMUs. Thus, this research aimed...

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Vydáno v:IEEE transactions on neural systems and rehabilitation engineering Ročník 29; s. 1280 - 1289
Hlavní autoři: Nazarahari, Milad, Rouhani, Hossein
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Accelerometers
body sensor network
Covariance matrix
Dead reckoning
Extended Kalman filter
Feet
Filters
Gravity
Gyroscopes
Human motion monitoring
inertial navigation systems
Inertial platforms
Magnetic disturbances
Magnetic field measurement
Magnetic separation
Magnetometers
Mathematical analysis
Mathematical model
Motion capture
Orientation
Pitch (inclination)
Quaternions
robust extended Kalman filter
Robustness
Rolling motion
sensor fusion algorithms
State vectors
Task analysis
Thigh
Tracking
Yaw
ISSN:1534-4320, 1558-0210, 1558-0210
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Popis
Shrnutí:Accurate and robust orientation estimation using magnetic and inertial measurement units (MIMUs) has been a challenge for many years in long-duration measurements of joint angles and pedestrian dead-reckoning systems and has limited several real-world applications of MIMUs. Thus, this research aimed at developing a full-state Robust Extended Kalman Filter (REKF) for accurate and robust orientation tracking with MIMUs, particularly during long-duration dynamic tasks. First, we structured a novel EKF by including the orientation quaternion, non-gravitational acceleration, gyroscope bias, and magnetic disturbance in the state vector. Next, the a posteriori error covariance matrix equation was modified to build a REKF. We compared the accuracy and robustness of our proposed REKF with four filters from the literature using optimal filter gains. We measured the thigh, shank, and foot orientation of nine participants while performing short- and long-duration tasks using MIMUs and a camera motion-capture system. REKF outperformed the filters from literature significantly (p < 0.05) in terms of accuracy and robustness for long-duration tasks. For example, for foot MIMU, the median RMSE of (roll, pitch, yaw) were (6.5, 5.5, 7.8) and (22.8, 23.9, 25) deg for REKF and the best filter from the literature, respectively. For short-duration trials, REKF achieved significantly (p < 0.05) better or similar performance compared to the literature. We concluded that including non-gravitational acceleration, gyroscope bias, and magnetic disturbance in the state vector, as well as using a robust filter structure, is required for accurate and robust orientation tracking, at least in long-duration tasks.
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ISSN:1534-4320
1558-0210
1558-0210
DOI:10.1109/TNSRE.2021.3093006