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Forward and Backward Reaching Inverse Kinematics (FABRIK) solver for DHM: A pilot study
University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment. University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment. (Interaction Lab)ORCID iD: 0000-0003-2254-1396
Texas Tech University, United States.
University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment. (Interaction Lab)ORCID iD: 0000-0002-6568-9342
University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment. (User Centred Product Design)ORCID iD: 0000-0003-4596-3815
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2022 (English)In: Proceedings of the 7th International Digital Human Modeling Symposium (DHM 2022), August 29–30, 2022, Iowa City, Iowa, USA, University of Iowa Press, 2022, Vol. 7, p. 1-11, article id 26Conference paper, Published paper (Refereed)
Abstract [en]

Posture/motion prediction is the basis of the human motion simulations that make up the core of many digital human modeling (DHM) tools and methods. With the goal of producing realistic postures and motions, a common element of posture/motion prediction methods involves applying some set of constraints to biomechanical models of humans on the positions and orientations of specified body parts. While many formulations of biomechanical constraints may produce valid predictions, they must overcome the challenges posed by the highly redundant nature of human biomechanical systems. DHM researchers and developers typically focus on optimization formulations to facilitate the identification and selection of valid solutions. While these approaches produce optimal behavior according to some, e.g., ergonomic, optimization criteria, these solutions require considerable computational power and appear vastly different from how humans produce motion. In this paper, we take a different approach and consider the Forward and Backward Reaching Inverse Kinematics (FABRIK) solver developed in the context of computer graphics for rigged character animation. This approach identifies postures quickly and efficiently, often requiring a fraction of the computation time involved in optimization-based methods. Critically, the FABRIK solver identifies posture predictions based on a lightweight heuristic approach. Specifically, the solver works in joint position space and identifies solutions according to a minimal joint displacement principle. We apply the FABRIK solver to a seven-degree of freedom human arm model during a reaching task from an initial to an end target location, fixing the shoulder position and providing the end effector (index fingertip) position and orientation from each frame of the motion capture data. In this preliminary study, predicted postures are compared to experimental data from a single human subject. Overall the predicted postures were very near the recorded data, with an average RMSE of 1.67°. Although more validation is necessary, we believe that the FABRIK solver has great potential for producing realistic human posture/motion in real-time, with applications in the area of DHM.

Place, publisher, year, edition, pages
University of Iowa Press, 2022. Vol. 7, p. 1-11, article id 26
Keywords [en]
Inverse Kinematics, Posture Prediction, IK validation, FABRIK
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
User Centred Product Design; Interaction Lab (ILAB)
Identifiers
URN: urn:nbn:se:his:diva-21830DOI: 10.17077/dhm.31772ISBN: 978-0-9840378-4-1 (print)OAI: oai:DiVA.org:his-21830DiVA, id: diva2:1697451
Conference
7th International Digital Human Modeling Symposium (DHM 2022), August 29–30, 2022, Iowa City, Iowa, USA. The conference was followed by the Iowa Virtual Human Summit 2022.
Note

Copyright © 2022 the author(s) 

Available from: 2022-09-20 Created: 2022-09-20 Last updated: 2022-10-17Bibliographically approved

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Lamb, MauriceBilling, ErikHögberg, Dan

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