← ArchivePaper2023
AddBiomechanics: Automating model scaling, inverse kinematics, and inverse dynamics from human motion data through sequential optimization
Keenon Werling, Nicholas A. Bianco, Michael Raitor, Jon Stingel, Jennifer L. Hicks, Steven H. Collins, Scott L. Delp, C. Karen Liu
Open-source cloud service that automates OpenSim skeleton model scaling, marker registration, inverse kinematics, and inverse dynamics from mocap data via bilevel optimization.
Abstract
Creating large-scale public datasets of human motion biomechanics could unlock data-driven breakthroughs in our understanding of human motion, neuromuscular diseases, and assistive devices. However, the manual effort currently required to process motion capture data and quantify the kinematics and dynamics of movement is costly and limits the collection and sharing of large-scale biomechanical datasets. We present a method, called AddBiomechanics, to automate and standardize the quantification of human movement dynamics from motion capture data. We use linear methods followed by a non-convex bilevel optimization to scale the body segments of a musculoskeletal model, register the locations of optical markers placed on an experimental subject to the markers on a musculoskeletal model, and compute body segment kinematics given trajectories of experimental markers during a motion. We then apply a linear method followed by another non-convex optimization to find body segment masses and fine tune kinematics to minimize residual forces given corresponding trajectories of ground reaction forces.
How to read this
- Category
- Tool / pipeline: automated biomechanics processing service
- Contributions
- An automated, standardized pipeline for model scaling, inverse kinematics, and inverse dynamics from motion capture data
- A linear-then-nonconvex bilevel optimization that scales musculoskeletal body segments and registers experimental markers to model markers
- A second linear-then-optimization stage that recovers segment masses and tunes kinematics to minimize residual forces given ground reaction forces
- Context
- Relates to biomechanically accurate digital-human modeling such as Keller's SKEL, packaging OpenSim-style scaling, IK, and ID into an automated cloud service to enable large-scale dataset sharing.Builds on: From Skin to Skeleton: Towards Biomechanically Accurate 3D Digital Humans
- Correctness
- Built on standard musculoskeletal modeling with non-convex bilevel optimization; results depend on marker and ground-reaction-force data quality, and residual-force minimization is an approximation, so dynamics estimates should be treated as model-dependent.
- Clarity
- Accessible at the goal level for a first pass; the bilevel optimization formulation needs a second, careful pass.
- How to read it
- First pass for the pipeline stages and what each optimization solves; do a second pass on the bilevel optimization and residual handling if you plan to process your own mocap.
Built upon by
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Related work
- Muscles in Time: Learning to Understand Human Motion by Simulating Muscle Activations 2024 / NeurIPS
- BioPose: Biomechanically-accurate 3D Pose Estimation from Monocular Videos 2025 / WACV
- From Skin to Skeleton: Towards Biomechanically Accurate 3D Digital Humans 2023 / SIGGRAPH Asia
- Automated Extraction and Parameterization of Motions in Large Data Sets 2004 / SIGGRAPH
Keywords
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