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Article

Inertial and magnetic posture tracking for inserting humans into networked virtual environments

Authors:

Eric R. Bachmann,

Robert B. McGhee,

Michael J. ZydaAuthors Info & Claims

VRST '01: Proceedings of the ACM symposium on Virtual reality software and technology

Pages 9 - 16

https://doi.org/10.1145/505008.505011

Published: 15 November 2001 Publication History

Abstract

Rigid body orientation can be determined without the aid of a generated source using nine-axis MARG (Magnetic field, Angular Rate, and Gravity) sensor unit containing three orthogonally mounted angular rate sensors, three orthogonal linear accelerometers and three orthogonal magnetometers. This paper describes a quaternion-based complementary filter algorithm for processing the output data from such a sensor. The filter forms the basis for a system designed to determine the posture of an articulated body in real-time. In the system the orientation relative to an Earth-fixed reference frame of each limb segment is individually determined through the use of an attached MARG sensor. The orientations are used to set the posture of an articulated body model. Details of the fabrication of a prototype MARG sensor are presented. Calibration algorithms for the sensors and the human body model are also presented. Experimental results demonstrate the effectiveness of the tracking system and verify the correctness of the underlying theory.

References

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Information

Published In

cover image ACM Conferences

VRST '01: Proceedings of the ACM symposium on Virtual reality software and technology

November 2001

214 pages

ISBN:1581134274

DOI:10.1145/505008

Conference Chairs:
Chris Shaw
Georgia Institute of Technology, USA
,
Wenping Wang
The University of Hong Kong, HK
,
General Chair:
Mark Green
City University of Hong Kong, HK

Copyright © 2001 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 15 November 2001

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VRST01

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VRST01: The ACM Symposium on Virtual Reality Software and Technology 2001

November 15 - 17, 2001

Alberta, Baniff, Canada

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Overall Acceptance Rate 66 of 254 submissions, 26%

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Cited By

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https://doi.org/10.1109/TVCG.2024.3464738
DeVrio NMollyn VHarrison C(2023)SmartPoser: Arm Pose Estimation with a Smartphone and Smartwatch Using UWB and IMU DataProceedings of the 36th Annual ACM Symposium on User Interface Software and Technology10.1145/3586183.3606821(1-11)Online publication date: 29-Oct-2023
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Bao YZhao XQian D(2023)FusePose: IMU-Vision Sensor Fusion in Kinematic Space for Parametric Human Pose EstimationIEEE Transactions on Multimedia10.1109/TMM.2022.322747225(7736-7746)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1109/TMM.2022.3227472
Shi YLi ZXuan LYin CZhang JZhang C(2022)Deep Learning-Based Recognition of Human Training States During Sit-to-Stand TransferJournal of Nanoelectronics and Optoelectronics10.1166/jno.2022.323717:4(634-641)Online publication date: 1-Apr-2022
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Jiang YYe YGopinath DWon JWinkler ALiu C(2022)Transformer Inertial Poser: Real-time Human Motion Reconstruction from Sparse IMUs with Simultaneous Terrain GenerationSIGGRAPH Asia 2022 Conference Papers10.1145/3550469.3555428(1-9)Online publication date: 29-Nov-2022
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