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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Veröffentlicht in:	IEEE sensors journal Jg. 21; H. 3; S. 3722 - 3729
Hauptverfasser:	Razavi, Hamidreza, Salarieh, Hassan, Alasty, Aria
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.02.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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
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Abstract	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.
AbstractList	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.
Author	Alasty, Aria Razavi, Hamidreza Salarieh, Hassan
Author_xml	– sequence: 1 givenname: Hamidreza orcidid: 0000-0002-0783-1689 surname: Razavi fullname: Razavi, Hamidreza email: h.razavi@outlook.com organization: Department of Mechanical Engineering, Sharif University of Technology, Tehran, Iran – sequence: 2 givenname: Hassan orcidid: 0000-0002-0604-5731 surname: Salarieh fullname: Salarieh, Hassan email: salarieh@sharif.edu organization: Department of Mechanical Engineering, Sharif University of Technology, Tehran, Iran – sequence: 3 givenname: Aria orcidid: 0000-0002-6354-0034 surname: Alasty fullname: Alasty, Aria email: aalasti@sharif.edu organization: Department of Mechanical Engineering, Sharif University of Technology, Tehran, Iran
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Cites_doi	10.1109/JSEN.2016.2589660 10.21105/joss.00026 10.1177/0954410019861778 10.1561/9781680833577 10.1109/JSEN.2016.2609392 10.1002/j.2161-4296.2011.tb01797.x 10.1109/TPAMI.2016.2522398 10.1109/ICRA.2018.8463176 10.1109/ICASSP.2001.940586 10.1109/JSEN.2016.2593011 10.2514/3.19717 10.1109/JSEN.2020.2982459 10.3390/s17071591 10.1109/JSEN.2020.2974900
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SubjectTerms	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
Title	Inertial Motion Capture Accuracy Improvement by Kalman Smoothing and Dynamic Networks
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