![]() ![]() ![]() 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. Three test cases are discussed: the Rabinowicz test and other two test problems casted for this occurrence.Muscle-driven simulations of human and animal motion are widely used to complement physical experiments for studying movement dynamics. The numerical performances and differences of each model have been monitored and compared. These are divided into two main categories: those based on the Coulomb approach and those established on the bristle analogy. This paper reports and compares eight widespread engineering friction force models. For mechanical systems, the computational efficiency of the algorithm is a critical issue when a fast and responsive dynamic computation is required. Features such as the capability to replicate stiction, Stribeck effect, and pre-sliding displacement are taken into account when selecting a friction formulation. Suitability of the model depends on physical and operating conditions. However, it should be acknowledged that each model has its own distinctive pros and cons. ![]() This paper focuses the attention on well-known friction models and offers a review and comparison based on numerical efficiency. Friction is a complex phenomenon depending on many physical parameters and working conditions, and none of the available models can claim general validity. Their choice affects the matching of numerical results with physically observed behavior. Pennestrì, Ettore Rossi, Valerio Salvini, Pietro Valentini, Pierįriction force models play a fundamental role for simulation of mechanical systems. Review and comparison of dry friction force models Review and comparison of dry friction force models ![]()
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