Inertial Motion Capture Accuracy Improvement by Kalman Smoothing and Dynamic Networks

Localization-capable inertial motion capture algorithms rely on zero-velocity updates (ZUPT), usually as measurements in a Kalman filtering scheme, for position and attitude error control. As ZUPTs are only applicable during the static phases a link goes through, estimation errors grow during dynami...

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Bibliographic Details
Published in:IEEE sensors journal Vol. 21; no. 3; pp. 3722 - 3729
Main Authors: Razavi, Hamidreza, Salarieh, Hassan, Alasty, Aria
Format: Journal Article
Language:English
Published: New York IEEE 01.02.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Attitudes
Biomechanics
Error analysis
Error reduction
Estimation error
Heuristic algorithms
inertial sensor network
Kalman filters
Motion capture
Optimization
Parameter estimation
Pelvis
Position measurement
Process parameters
Quaternions
Sensor fusion
Sensors
Smoothing
Smoothing methods
unscented smoothing
Walking
ISSN:1530-437X, 1558-1748
Online Access:Get full text
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Summary:Localization-capable inertial motion capture algorithms rely on zero-velocity updates (ZUPT), usually as measurements in a Kalman filtering scheme, for position and attitude error control. As ZUPTs are only applicable during the static phases a link goes through, estimation errors grow during dynamic ones. This error growth may somewhat be mitigated by imposing biomechanical constraints in multi-sensor systems. Error reduction is also possible by optimization-based methods that incorporate the dynamic and static constraints governing the system behavior over a period of time (e.g. the dynamic network algorithm); when this period includes multiple static phases for a link, its estimation accuracy is greatly improved. The current study enhances the error control capabilities of an existing inertial motion capture algorithm by multi-stage smoothing. The base algorithm benefits from imposing biomechanical constraints and is self-calibrating with respect to body geometry and some sensor parameters. The smoothing process, conducted over the stepping periods of each foot, comprises two stages; Kalman smoothing followed by error minimization by dynamic networks. The performance of the algorithm, deployed using both extended and square-root unscented Kalman filtering schemes (EKF and SRUKF, respectively), is experimentally evaluated during a fast-paced walking test using a custom-made inertial motion capture system. A comparison with an optical motion capture system showed that the proposed method decreased pelvis position and attitude estimation errors by 19% and 29%, respectively. Furthermore, compared to the EKF-based smoothing algorithm, the SRUKF-based method proved to be more successful in error reduction and parameter estimation.
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ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2020.3024874