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

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.

Man/machine interface based on the discharge timings of spinal motor neurons after targeted muscle reinnervation

“…The open-source software OpenSim 44 was used to scale a generic upper extremity model of the musculoskeletal geometry 45 to match the patient's anthropometry. The musculoskeletal geometry model had seven upper extremity degrees of freedom and incorporated a total of 14 muscle-tendon units, spanning the shoulder, elbow, wrist and hand joints (Fig.…”

An Uncontrolled Manifold Analysis of Arm Joint Variability in Virtual Planar Position and Orientation Telemanipulation

“…To estimate the arm joint configuration, OpenSim's inverse kinematics and a widely adopted upper limb musculoskeletal model were adopted [45]- [47]. Raw marker movements were filtered (recursive 2nd order Butterworth filter with cut-off frequency 6Hz) and static acquisitions were used to scale the model to fit the users' arm and thorax dimensions.…”

Computing Muscle, Ligament, and Osseous Contributions to the Elbow Varus Moment During Baseball Pitching

“…To update the whole-body model, the three-dimensional surface geometry, mass, and inertial properties of the ulna, radius, eight carpal bones, five metacarpal bones, and 14 phalanges were redefined using data files available from a validated, open-source, dynamic model of the upper limb that is also representative of a 50th percentile male. 35 In addition, a baseball was added to the model by defining a static homogeneous transformation between the modeled right middle metacarpal and a spherical mass, such that the hand and ball were coincident. The baseball had dimensions specified by the official major league baseball rules 1 and the inertia of a solid sphere.…”

“…The kinematic description of the right shoulder joint was the same as the open-source model for lower-extremity tasks, 16 but the order of the three shoulder rotations were updated to match typical descriptions of shoulder kinematics in pitching literature (adduction, flexion, and then rotation). 11 Joint kinematic constraints for elbow flexion and forearm pronation were identical to the open-source upper-limb model 35 and were defined by humeroulnar hinge and radioulnar hinge joints, respectively. A varus-valgus DOF (Figure 3) was added to the elbow by defining an additional humeroulnar hinge joint at the lateral edge of the proximal surface of the ulna.…”

“…The hand was fixed in a neutral grip posture as described in the open- source upper-limb model. 35 From examination of the raw motion data, it was determined that wrist and hand motion during this phase of the pitching motion was minor and these simplifications were made to substantially increase our computation efficiency.…”