Rapid bilevel optimization to concurrently solve musculoskeletal scaling, marker registration, and inverse kinematic problems for human motion reconstruction
Authors: Werling Keenon , Raitor Michael , Stingel Jon , Hicks Jennifer L. , Collins Steve , Delp Scott L. , Liu C. Karen 
Published: August 2022

AbstractCreating 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 is costly and limits the collection and sharing of large-scale biomechanical datasets. We present a method to automate and standardize motion capture data processing: bilevel optimization that is able 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. The optimization requires less than five minutes of computation to process a subject’s motion capture data, compared with about one day of manual work for a human expert. On a sample of 34 trials of experimental data, the root-mean-square marker reconstruction error (RMSE) was 1.38 cm, approximately 40% lower than the 2.58 cm achieved manually by 3 experts. Optimization solutions reconstructed known joint angle trajectories from four diverse motion trials of synthetic data to an average of 0.79 degrees RMSE. We have published an open source cloud service at AddBiomechanics.org to process experimental motion capture data, which is available at no cost and asks that users agree to share processed and de-identified data with the community. Reducing the barriers to processing and sharing high-quality human motion biomechanics data will enable more people to engage in state-of-the-art biomechanical analysis in their work, do so at lower cost, and share larger and more accurate datasets.Author summaryCreating large-scale public datasets of human motion could unlock data-driven breakthroughs in our understanding of neuromuscular diseases, assistive devices, and human motion more broadly. The manual effort currently required to process these motion datasets is costly and limits the collection and sharing of large-scale datasets. Our cloud-based software tool, called AddBiomechanics, uses state-of-the-art optimization techniques to automatically scale the body segments of a musculoskeletal model to match the subject of interest, and then compute body segment kinematics during a motion. The optimization requires less than five minutes of computation to process a subject’s motion capture data, compared with about one day of manual work for a human expert. The accuracy of the approach in quantifying the body segment kinematics is as good or better than the results achieved manually by experts. Reducing the barriers to processing and sharing high-quality human motion biomechanics data will enable more people to engage in state-of-the-art biomechanical analysis, do so at lower cost, and share larger and more accurate datasets.