An extended complementary filter for full-body MARG orientation estimation
An extended complementary filter for full-body MARG orientation estimation
Inertial sensing suites now permeate all forms of smart automation, yet a plateau exists in the real-world derivation of global orientation. Magnetic field fluctuations and inefficient sensor fusion still inhibit deployment. In this article, we introduce a new algorithm, an extended complementary filter (ECF), to derive 3-D rigid body orientation from inertial sensing suites addressing these challenges. The ECF combines computational efficiency of classic complementary filters with improved accuracy compared to popular optimization filters. We present a complete formulation of the algorithm, including an extension to address the challenge of orientation accuracy in the presence of fluctuating magnetic fields. Performance is tested under a variety of conditions and benchmarked against the commonly used gradient decent inertial sensor fusion algorithm. Results demonstrate improved efficiency, with the ECF achieving convergence 30% faster than standard alternatives. We further demonstrate an improved robustness to sources of magnetic interference in pitch and roll and to fast changes of orientation in the yaw direction. The ECF has been implemented at the core of a wearable rehabilitation system tracking movement of stroke patients for home telehealth. The ECF and accompanying magnetic disturbance rejection algorithm enables previously unachievable real-time patient movement feedback in the form of a full virtual human (avatar), even in the presence of magnetic disturbance. Algorithm efficiency and accuracy have also spawned an entire commercial product line released by the company x-io. We believe the ECF and accompanying magnetic disturbance routines are key enablers for future widespread use of wearable systems with the capacity for global orientation tracking.
Inertial measurement, motion tracking, navigation, rehabilitation, robotics, sensor fusion, telehealth (TH), wearables
2054-2064
Madgwick, Sebastian O. H.
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Wilson, Samuel
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Turk, Ruth
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Burridge, Jane
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Kapatos, Christos
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Vaidyanathan, Ravi
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28 May 2020
Madgwick, Sebastian O. H.
fc053520-8c0b-4ca2-a439-45cf953e489c
Wilson, Samuel
b5b35cff-7d54-4df2-bfef-9cb9dd1114b8
Turk, Ruth
9bb21965-6f9f-4c9c-8505-94df8e168f52
Burridge, Jane
0110e9ea-0884-4982-a003-cb6307f38f64
Kapatos, Christos
ff25e8a9-09c0-4e8f-bc05-10337d11ce36
Vaidyanathan, Ravi
7758a2b8-34be-499d-a4f0-41aa4cae70d2
Madgwick, Sebastian O. H., Wilson, Samuel, Turk, Ruth, Burridge, Jane, Kapatos, Christos and Vaidyanathan, Ravi
(2020)
An extended complementary filter for full-body MARG orientation estimation.
IEEE/ASME Transactions on Mechatronics, 25 (4), , [9103115].
(doi:10.1109/TMECH.2020.2992296).
Abstract
Inertial sensing suites now permeate all forms of smart automation, yet a plateau exists in the real-world derivation of global orientation. Magnetic field fluctuations and inefficient sensor fusion still inhibit deployment. In this article, we introduce a new algorithm, an extended complementary filter (ECF), to derive 3-D rigid body orientation from inertial sensing suites addressing these challenges. The ECF combines computational efficiency of classic complementary filters with improved accuracy compared to popular optimization filters. We present a complete formulation of the algorithm, including an extension to address the challenge of orientation accuracy in the presence of fluctuating magnetic fields. Performance is tested under a variety of conditions and benchmarked against the commonly used gradient decent inertial sensor fusion algorithm. Results demonstrate improved efficiency, with the ECF achieving convergence 30% faster than standard alternatives. We further demonstrate an improved robustness to sources of magnetic interference in pitch and roll and to fast changes of orientation in the yaw direction. The ECF has been implemented at the core of a wearable rehabilitation system tracking movement of stroke patients for home telehealth. The ECF and accompanying magnetic disturbance rejection algorithm enables previously unachievable real-time patient movement feedback in the form of a full virtual human (avatar), even in the presence of magnetic disturbance. Algorithm efficiency and accuracy have also spawned an entire commercial product line released by the company x-io. We believe the ECF and accompanying magnetic disturbance routines are key enablers for future widespread use of wearable systems with the capacity for global orientation tracking.
Text
09103115
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Accepted/In Press date: 25 April 2020
Published date: 28 May 2020
Keywords:
Inertial measurement, motion tracking, navigation, rehabilitation, robotics, sensor fusion, telehealth (TH), wearables
Identifiers
Local EPrints ID: 445532
URI: http://eprints.soton.ac.uk/id/eprint/445532
ISSN: 1083-4435
PURE UUID: 11ab371f-8162-4269-a95d-3857b4a276f8
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Date deposited: 15 Dec 2020 17:30
Last modified: 17 Mar 2024 02:59
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Contributors
Author:
Sebastian O. H. Madgwick
Author:
Samuel Wilson
Author:
Christos Kapatos
Author:
Ravi Vaidyanathan
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