Our approach can handle all commonly used musculotendon path types, including those with multiple path points and wrapping surfaces. We express the motion of the musculotendons in terms of the motion of the skeletal joints using a chain of Jacobians, so that at the top level, only the reduced degrees of freedom of the skeleton are used to completely drive both bones and musculotendons. We approximate the inertia of the muscle by assuming that muscle mass is distributed along the centerline of the muscle. We propose a simple and practical approach for incorporating the effects of muscle inertia, which has been ignored by previous musculoskeletal simulators in both graphics and biomechanics. The numerical examples demonstrate that the proposed framework can effectively identify subject-specific muscle parameters, and the trained physics-informed forward-dynamics surrogate yields motion prediction of elbow flexion-extension motion in good agreement with the measured joint motion data. A physics-informed neural network is trained to relate sEMG signals to motion in the low dimensional feature space and simultaneously identify key MSK parameters. Features of the high-dimensional noisy muscle excitation sEMG signals are used to construct a low-dimensional representation for enhancement of forward dynamics prediction. In this work, we propose a feature encoded physics-informed parameter identification neural network (FEPI-PINN) for simultaneous prediction of motion and parameter identification of human MSK systems. While machine learning approaches with capabilities in extracting complex features and patterns from large amount of data have been applied to motion prediction given sEMG signals, the learnt data-driven mapping is black-box and may not satisfy the underlying physics. Identification of muscle-tendon force generation properties and muscle activities from physiological measurements, e.g., motion data and raw surface electromyography (sEMG), offers the opportunities for construction of a subject-specific musculoskeletal (MSK) digital twin system for health conditions assessment and human motion prediction. To encourage further development of musculotendon models, we provide implementations of each of these models in OpenSim version 3.1 and benchmark data online, enabling others to reproduce our results and test their models of musculotendon dynamics. When compared to forces generated by submaximally-activated biological muscle, the forces produced by the equilibrium, damped equilibrium, and rigid-tendon models have mean absolute errors less than 16.2%, 16.4%, and 18.5%, respectively. The equilibrium, damped equilibrium, and rigid-tendon models reproduce forces generated by maximally-activated biological muscle with mean absolute errors less than 8.9%, 8.9%, and 20.9% of the maximum isometric muscle force, respectively. In the special case of simulating a muscle with a short tendon, the rigid-tendon model produces forces that match those generated by the elastic-tendon models, but simulates 2-54 times faster when an explicit integrator is used and 6-31 times faster when an implicit integrator is used. At low activation, the damped equilibrium model is 29 times faster than the equilibrium model when using an explicit integrator and 3 times faster when using an implicit integrator at high activation, the two models have similar simulation speeds. Our simulation benchmarks demonstrate that the equilibrium and damped equilibrium models produce similar force profiles but have different computational speeds. Here we compare the speed and accuracy of three musculotendon models: two with an elastic tendon (an equilibrium model and a damped equilibrium model) and one with a rigid tendon. Musculotendon models are an essential component of muscle-driven simulations, yet neither the computational speed nor the biological accuracy of the simulated forces has been adequately evaluated. Muscle-driven simulations of human and animal motion are widely used to complement physical experiments for studying movement dynamics.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |