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@article{faucris.280977257,
abstract = {Optimal control simulations of musculoskeletal models can be used to
reconstruct motions measured with optical motion capture to estimate
joint and muscle kinematics and kinetics. These simulations are mutually
and dynamically consistent, in contrast to traditional inverse methods.
Commonly, optimal control simulations are generated by tracking
generalized coordinates in combination with ground reaction forces. The
generalized coordinates are estimated from marker positions using, for
example, inverse kinematics. Hence, inaccuracies in the estimated
coordinates are tracked in the simulation. We developed an approach to
reconstruct arbitrary motions, such as change of direction motions,
using optimal control simulations of 3D full-body musculoskeletal models
by directly tracking marker and ground reaction force data. For
evaluation, we recorded three trials each of straight running, curved
running, and a v-cut for 10 participants. We reconstructed the
recordings with marker tracking simulations, coordinate tracking
simulations, and inverse kinematics and dynamics. First, we analyzed the
convergence of the simulations and found that the wall time increased
three to four times when using marker tracking compared to coordinate
tracking. Then, we compared the marker trajectories, ground reaction
forces, pelvis translations, joint angles, and joint moments between the
three reconstruction methods. Root mean squared deviations between
measured and estimated marker positions were smallest for inverse
kinematics (*e.g*., 7.6 ± 5.1 mm for v-cut). However, measurement
noise and soft tissue artifacts are likely also tracked in inverse
kinematics, meaning that this approach does not reflect a gold standard.
Marker tracking simulations resulted in slightly higher root mean
squared marker deviations (*e.g*., 9.5 ± 6.2 mm for v-cut) than
inverse kinematics. In contrast, coordinate tracking resulted in
deviations that were nearly twice as high (*e.g*., 16.8 ± 10.5 mm
for v-cut). Joint angles from coordinate tracking followed the estimated
joint angles from inverse kinematics more closely than marker tracking (*e.g*., root mean squared deviation of 1.4 ± 1.8 deg *vs*.
3.5 ± 4.0 deg for v-cut). However, we did not have a gold standard
measurement of the joint angles, so it is unknown if this larger
deviation means the solution is less accurate. In conclusion, we showed
that optimal control simulations of change of direction running motions
can be created by tracking marker and ground reaction force data. Marker
tracking considerably improved marker accuracy compared to coordinate
tracking. Therefore, we recommend reconstructing movements by directly
tracking marker data in the optimal control simulation when precise
marker tracking is required.},
author = {Nitschke, Marlies and Marzilger, Robert and Leyendecker, Sigrid and Eskofier, Björn and Koelewijn, Anne},
doi = {10.7717/peerj.14852},
faupublication = {yes},
journal = {PeerJ},
peerreviewed = {Yes},
title = {{Change} the direction: {3D} optimal control simulation by directly tracking marker and ground reaction force data},
url = {https://peerj.com/articles/14852/},
year = {2023}
}