Benchmarking of dynamic simulation predictions in two software platforms using an upper limb musculoskeletal model

Saul, Katherine R.; Hu, Xiao; Goehler, Craig M.; Vidt, Meghan E.; Daly, Melissa; Velisar, Anca; Murray, Wendy M.

Affiliations

¹Mechanical and Aerospace Engineering Department, North Carolina State University, Raleigh, NC 27695, USA

²Sensory MotorPerformance Program, Rehabilitation Institute of Chicago, Chicago, IL 60611, USA

³Department of Mechanical Engineering, Valparaiso University, Valparaiso, IN 46383, USA

⁴Biomedical Engineering Department, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA

⁵Virginia Tech – Wake Forest University School of Biomedical Engineering and Sciences, Winston-Salem, NC27157, USA

⁶Department of Neurology and Neurosurgery, School of Medicine, Stanford University, Stanford, CA 94305, USA

⁷Department of Biomedical Engineering, Northwestern University, Chicago, IL 60611, USA

⁸Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL 60611, USA

⁹Department of Physical Therapy and Human Movement Sciences, Northwestern University, Chicago, IL 60611, USA

¹⁰Research Service, Edward Hines, Jr. VA Hospital, Hines, IL 60141, USA

Abstract and keywords

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.

Keywords: biomechanics; computational modeling; medical computing; musculoskeletal; neuromuscular

Links

DOI: 10.1080/10255842.2014.916698

PubMed: 24995410

WoS: 000347157000008

History

Received: 2013-04-09

Accepted: 2014-04-16

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2024	Mattar LT, Mahboobin AB, Popchak AJ, Anderst WJ, Musahl V, Irrgang JJ, Debski RE. Individuals with rotator cuff tears unsuccessfully treated with exercise therapy have less inferiorly oriented net muscle forces during scapular plane abduction. J Biomech. January 2024;162:111859.
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2022	Vancleef S, Wesseling M, Vander Sloten J, Jonkers I. Musculoskeletal modeling-based definition of load cases and worst-case fracture orientation for the design of clavicle fixation plates. J Orthop Res. September 2022;40(9):2179-2188.
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2019	Dixit NN. Morphological Changes to the Glenohumeral Musculoskeletal System Following Brachial Plexus Birth Injury (BPBI) [PhD thesis]. North Carolina State University; 2019.
2020	Jadelis CT. Index Finger Extensor Hood Mechanics Using Finite Element and Rigid Body Dynamic Modeling [Master's thesis]. North Carolina State University; 2020.
2015	Nelson CM. An Investigation of Musculoskeletal Adaptation At the Paretic Elbow After Chronic Hemiparetic Stroke Using Imaging, Biomechanical and Simulation Approaches [PhD thesis]. Evanston, IL: Northwestern University; December 2015.
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2015	Santago AC II. Implications of Aging and Degenerative Rotator Cuff Tears for Upper Limb Muscle Morphology, Strength, and Function [PhD thesis]. Winston-Salem, NC: Wake Forest University; May 2015.