Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/81197
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dc.contributor.authorWilson, Sen_US
dc.contributor.authorEberle, Hen_US
dc.contributor.authorHayashi, Yen_US
dc.contributor.authorMadgwick, SOHen_US
dc.contributor.authorMcGregor, Aen_US
dc.contributor.authorJing, Xen_US
dc.contributor.authorVaidyanathan, Ren_US
dc.date.accessioned2019-08-23T08:29:42Z-
dc.date.available2019-08-23T08:29:42Z-
dc.date.issued2019-
dc.identifier.citationMechanical systems and signal processing, 2019, v. 130, p. 183-200en_US
dc.identifier.issn0888-3270-
dc.identifier.urihttp://hdl.handle.net/10397/81197-
dc.description.abstractWe introduce a novel magnetic angular rate gravity (MARG) sensor fusion algorithm for inertial measurement. The new algorithm improves the popular gradient descent (ʻMadgwick’) algorithm increasing accuracy and robustness while preserving computational efficiency. Analytic and experimental results demonstrate faster convergence for multiple variations of the algorithm through changing magnetic inclination. Furthermore, decoupling of magnetic field variance from roll and pitch estimation is proven for enhanced robustness. The algorithm is validated in a human-machine interface (HMI) case study. The case study involves hardware implementation for wearable robot teleoperation in both Virtual Reality (VR) and in real-time on a 14 degree-of-freedom (DoF) humanoid robot. The experiment fuses inertial (movement) and mechanomyography (MMG) muscle sensing to control robot arm movement and grasp simultaneously, demonstrating algorithm efficacy and capacity to interface with other physiological sensors. To our knowledge, this is the first such formulation and the first fusion of inertial measurement and MMG in HMI. We believe the new algorithm holds the potential to impact a very wide range of inertial measurement applications where full orientation necessary. Physiological sensor synthesis and hardware interface further provides a foundation for robotic teleoperation systems with necessary robustness for use in the field.en_US
dc.description.sponsorshipDepartment of Mechanical Engineeringen_US
dc.language.isoenen_US
dc.publisherAcademic Pressen_US
dc.relation.ispartofMechanical systems and signal processingen_US
dc.rights© 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)en_US
dc.rightsThe following publication Wilson, S., Eberle, H., Hayashi, Y., Madgwick, S. O., McGregor, A., Jing, X., & Vaidyanathan, R. (2019). Formulation of a new gradient descent MARG orientation algorithm: Case study on robot teleoperation. Mechanical Systems and Signal Processing, 130, 183-200 is available at https://doi.org/10.1016/j.ymssp.2019.04.064en_US
dc.subjectHuman-machine interface (HMI)en_US
dc.subjectinertial measurement unit (IMU)en_US
dc.subjectInertial sensor fusionen_US
dc.subjectmechatronic sensingen_US
dc.subjectRobot teleoperationen_US
dc.subjectWearable sensorsen_US
dc.titleFormulation of a new gradient descent MARG orientation algorithm : case study on robot teleoperationen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage183-
dc.identifier.epage200-
dc.identifier.volume130-
dc.identifier.doi10.1016/j.ymssp.2019.04.064-
dc.identifier.scopus2-s2.0-85065389527-
dc.identifier.eissn1096-1216-
dc.description.validate201908 bcma-
dc.description.oapublished_final-
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