New Advances in Multibody Dynamics Simulation of the Musculoskeletal System:From Data-Driven to Data-Physics Hybrid Approaches
10.16156/j.1004-7220.2025.02.001
- VernacularTitle:肌骨系统多体动力学仿真新进展:从数据驱动到数据-物理耦合驱动
- Author:
Wenxuan CHEN
1
;
Weiyan REN
1
;
Jie YAO
1
;
Fang PU
1
Author Information
1. 生物力学与力生物学教育部重点实验室;高端医疗装备与器械创新及转化工业和信息化部重点实验室;国家医学攻关(医工结合方向)高端医疗装备与器械产教融合创新平台;北京航空航天大学 生物与医学工程学院,北京 100191
- Publication Type:Journal Article
- Keywords:
musculoskeletal system;
multibody dynamics;
data-driven models;
physics-informed neural network;
biomechanical modeling
- From:
Journal of Medical Biomechanics
2025;40(2):255-262
- CountryChina
- Language:Chinese
-
Abstract:
Multibody dynamics simulation of the musculoskeletal system is an essential tool for analyzing the biomechanical mechanisms underlying human motion.Recent research trends have shifted from traditional physics-based models toward data-driven or data-physics hybrid frameworks.This review presents the latest developments in these areas.Physics-based multibody dynamics simulations have undergone significant progress in terms of simulation fidelity,optimization algorithms,and software tools.However,their practical implementation remains constrained by the need for complex experimental data and the computational expense of solving differential equations.Conversely,data-driven method bolstered by advancements in deep learning have demonstrated remarkable efficiency in predicting joint angles,postures,ground reaction forces,joint torques,and muscle forces,as well as developing control algorithms for exoskeletons.However,despite these advantages,data-driven approaches face challenges such as limited generalizability and potential violation of biomechanical principles.To address these limitations,data-physics hybrid approaches(e.g.,physics-informed neural network,PINN)which integrate physical constraints(e.g.,Newton-Euler equations,muscle constitutive laws)with data-driven architectures have been developed.This synergy enhances prediction accuracy while preserving the biological plausibility of solutions.Nevertheless,critical challenges persist,including the integration of multi-scale physical equations and the modeling of multi-joint coordination dynamics.Future research should prioritize:optimizing hybrid model architectures to balance computational efficiency and mechanistic accuracy,incorporating markerless motion capture techniques to improve real-world applicability,exploiting multi-scale physics and personalized parameter inversion to advance precision rehabilitation and motion analysis.These efforts will foster innovations in intelligent rehabilitation systems,clinical motion assessment,and related translational fields.
