Data-driven models of human body inertia
department or school: Science, Faculty of
department or school: Computer Science, Department of
data provider: DSpace
degree campus: UBCV
creator: Chen, Guanxiong
issued: 2024-04-29T16:12:09Z
issued: 2024
related degree: Master of Science - MSc
degree grantor: University of British Columbia
description: Accurate estimation of mass properties of the human musculoskeletal system is of great interest to many tasks, from gait analysis in biomechanics to motion tracking and control in computer animation. Previous work typically simplified the human musculoskeletal structure as a chain of rigid capsules, with muscle mass lumped with body segments. Such simplifications lead to errors in the system’s inertia matrix, and the error propagates to torque and pose estimates. In this study, we show that we can estimate the generalized joint-space inertia matrix of a human in motion, using a deep neural network or with a simple statistical model. The models do not make any assumptions other than that effective inertia matrices must be symmetric and positive definite. The models are trained and tested with real-world human data that includes synchronized motion and ground reaction forces. We show that a joint-space inertia matrix estimated from data can be physically plausible by revealing inertial coupling which a rigid, lumped inertia matrix fails to entail, and that effective inertia estimates are motion-type dependent. Moreover, we show that our neural inertia model SPDNet can predict inertia matrices parametrized by pose, body mass and height, that its predicted matrices are physically plausible, and that it generalizes well to unseen poses and mass distributions when used to reconstruct motion.
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/88062
note: Data-driven Models of Human Body InertiabyGuanxiong ChenB.A.Sc. in Computer Engineering, The University of British Columbia, 2021A THESIS SUBMITTED IN PARTIAL FULFILLMENTOF THE REQUIREMENTS FOR THE DEGREE OFMaster of ScienceinTHE FACULTY OF GRADUATE AND POSTDOCTORALSTUDIES(Computer Science)The University of British Columbia(Vancouver)April 2024© Guanxiong Chen, 2024The following individuals certify that they have read, and recommend to the Fac-ulty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled:Data-driven Models of Human Body Inertiasubmitted by Guanxiong Chen in partial fulfillment of the requirements for thedegree of Master of Science in Computer Science.Examining Committee:Dinesh K. Pai, Professor, Department of Computer Science, The University ofBritish ColumbiaSupervisorHelge Rhodin, Professor, Faculty of Technology, Bielefeld UniversitySupervisory Committee MemberiiAbstractAccurate estimation of mass properties of the human musculoskeletal system is ofgreat interest to many tasks, from gait analysis in biomechanics to motion trackingand control in computer animation. Previous work typically simplified the humanmusculoskeletal structure as a chain of rigid capsules, with muscle mass lumpedwith body segments. Such simplifications lead to errors in the system’s inertia ma-trix, and the error propagates to torque and pose estimates. In this study, we showthat we can estimate the generalized joint-space inertia matrix of a human in mo-tion, using a deep neural network or with a simple statistical model. The models donot make any assumptions other than that effective inertia matrices must be sym-metric and positive definite. The models are trained and tested with real-world hu-man data that includes synchronized motion and ground reaction forces. We showthat a joint-space inertia matrix estimated from data can be physically plausible byrevealing inertial coupling which a rigid, lumped inertia matrix fails to entail, andthat effective inert…
has type: Thesis/Dissertation
date issued: 2024-05
is shown at: 10.14288/1.0442032
language: eng
degree discipline: Computer Science
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
contributor: Pai, D. K. (Dinesh K.)
type: Text
identifier URI: http://hdl.handle.net/2429/88062