Benchmarking of dynamic simulation predictions in two software platforms using an upper limb musculoskeletal model |
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| Authors: | Katherine R. Saul Xiao Hu Craig M. Goehler Meghan E. Vidt Melissa Daly Anca Velisar |
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| Affiliation: | 1. Mechanical and Aerospace Engineering Department, North Carolina State University, Raleigh, NC 27695, USAksaul@ncsu.edu;3. Sensory Motor Performance Program, Rehabilitation Institute of Chicago, Chicago, IL 60611, USA;4. Department of Mechanical Engineering, Valparaiso University, Valparaiso, IN 46383, USA;5. Biomedical Engineering Department, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA;6. Virginia Tech – Wake Forest University School of Biomedical Engineering and Sciences, Winston-Salem, NC 27157, USA;7. Department of Neurology and Neurosurgery, School of Medicine, Stanford University, Stanford, CA 94305, USA |
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| Abstract: | Several opensource or commercially available software platforms are widely used to develop dynamic simulations of movement. While computational approaches are conceptually similar across platforms, technical differences in implementation may influence output. We present a new upper limb dynamic model as a tool to evaluate potential differences in predictive behavior between platforms. We evaluated to what extent differences in technical implementations in popular simulation software environments result in differences in kinematic predictions for single and multijoint movements using EMG- and optimization-based approaches for deriving control signals. We illustrate the benchmarking comparison using SIMM–Dynamics Pipeline–SD/Fast and OpenSim platforms. The most substantial divergence results from differences in muscle model and actuator paths. This model is a valuable resource and is available for download by other researchers. The model, data, and simulation results presented here can be used by future researchers to benchmark other software platforms and software upgrades for these two platforms. |
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| Keywords: | biomechanics computational modeling medical computing musculoskeletal neuromuscular |
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