PG-BIG: Personalized Guidance for Biomechanically Informed Generative Models in Exercise Science

Nicholas C King, Jared Maeyama, Shubh Maheshwari, Andrew Mcculloch, Rose Yu
Proceedings of The 8th Annual Learning for Dynamics and Control Conference, PMLR 331:1447-1469, 2026.

Abstract

Modeling human motion that is both biomechanically realistic and personalized to individual characteristics remains a key challenge in movement science. While biomechanically informed models such as BIGE incorporate physiological constraints to produce physically plausible motions, they operate at a population level and fail to capture individual variability in anatomy, strength, and motor strategy. Limiting their applicability to contexts like athletic performance analysis and rehabilitation, where personalization is critical. We introduce PG-BIG, a generative framework that integrates subject-specific personalization with biomechanical guidance in a unified generative pipeline. PG-BIG conditions on both an athlete profile and an action label to generate motion that aligns with individual style while maintaining physiological plausibility. Experiments on the Motus Global movement-screen dataset show that PG-BIG outperforms prior generative baselines in biomechanical realism and stylistic fidelity, enabling interpretable and personalized motion synthesis for applications in performance optimization and injury prevention.

Cite this Paper

BibTeX
@InProceedings{pmlr-v331-king26a, title = {PG-BIG: Personalized Guidance for Biomechanically Informed Generative Models in Exercise Science}, author = {King, Nicholas C and Maeyama, Jared and Maheshwari, Shubh and Mcculloch, Andrew and Yu, Rose}, booktitle = {Proceedings of The 8th Annual Learning for Dynamics and Control Conference}, pages = {1447--1469}, year = {2026}, editor = {Sukhatme, Gaurav and Lindemann, Lars and Tu, Stephen and Wierman, Adam and Atanasov, Nikolay}, volume = {331}, series = {Proceedings of Machine Learning Research}, month = {17--19 Jun}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v331/main/assets/king26a/king26a.pdf}, url = {https://proceedings.mlr.press/v331/king26a.html}, abstract = {Modeling human motion that is both biomechanically realistic and personalized to individual characteristics remains a key challenge in movement science. While biomechanically informed models such as BIGE incorporate physiological constraints to produce physically plausible motions, they operate at a population level and fail to capture individual variability in anatomy, strength, and motor strategy. Limiting their applicability to contexts like athletic performance analysis and rehabilitation, where personalization is critical. We introduce PG-BIG, a generative framework that integrates subject-specific personalization with biomechanical guidance in a unified generative pipeline. PG-BIG conditions on both an athlete profile and an action label to generate motion that aligns with individual style while maintaining physiological plausibility. Experiments on the Motus Global movement-screen dataset show that PG-BIG outperforms prior generative baselines in biomechanical realism and stylistic fidelity, enabling interpretable and personalized motion synthesis for applications in performance optimization and injury prevention.} }
Endnote
%0 Conference Paper %T PG-BIG: Personalized Guidance for Biomechanically Informed Generative Models in Exercise Science %A Nicholas C King %A Jared Maeyama %A Shubh Maheshwari %A Andrew Mcculloch %A Rose Yu %B Proceedings of The 8th Annual Learning for Dynamics and Control Conference %C Proceedings of Machine Learning Research %D 2026 %E Gaurav Sukhatme %E Lars Lindemann %E Stephen Tu %E Adam Wierman %E Nikolay Atanasov %F pmlr-v331-king26a %I PMLR %P 1447--1469 %U https://proceedings.mlr.press/v331/king26a.html %V 331 %X Modeling human motion that is both biomechanically realistic and personalized to individual characteristics remains a key challenge in movement science. While biomechanically informed models such as BIGE incorporate physiological constraints to produce physically plausible motions, they operate at a population level and fail to capture individual variability in anatomy, strength, and motor strategy. Limiting their applicability to contexts like athletic performance analysis and rehabilitation, where personalization is critical. We introduce PG-BIG, a generative framework that integrates subject-specific personalization with biomechanical guidance in a unified generative pipeline. PG-BIG conditions on both an athlete profile and an action label to generate motion that aligns with individual style while maintaining physiological plausibility. Experiments on the Motus Global movement-screen dataset show that PG-BIG outperforms prior generative baselines in biomechanical realism and stylistic fidelity, enabling interpretable and personalized motion synthesis for applications in performance optimization and injury prevention.
APA
King, N.C., Maeyama, J., Maheshwari, S., Mcculloch, A. & Yu, R.. (2026). PG-BIG: Personalized Guidance for Biomechanically Informed Generative Models in Exercise Science. Proceedings of The 8th Annual Learning for Dynamics and Control Conference, in Proceedings of Machine Learning Research 331:1447-1469 Available from https://proceedings.mlr.press/v331/king26a.html.

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