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On optical data-guided optimal control simulations of human motion

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Abstract

This work addresses the synergistic fusion of optimal control simulations and marker-based optical measurements of human motion. The latter is a widespread capturing technology in biomechanics and movement science. In the context of optimal control simulations, the idea is to improve the computational performance by using a realistic initial guess and to increase the realism of the simulated motion through data-guiding. In the context of motion capturing, the idea is to use biomechanical simulations in order to maintain accurate capturings also with reduced measurement frequencies and points. This would greatly improve the usability of such systems in terms of setup time and wearing comfort. In this work, we investigate different methods for combining physical laws, 3D marker positions obtained from the optical system, and physiologically motivated objectives in an optimal control framework. Moreover, we explore the potential of obtaining reasonable results—in terms of motion trajectories and torques that are close to reference obtained from using all available information—with a reduced measurement frequency and a reduced number of markers. The tests are performed on a human steering and throwing motion, where a human arm was captured with seven retroreflective markers at \(120\text{ Hz}\). Our results show, that a significant reduction of exploited measurements still provides feasible simulation results in our proposed method, given that the physiologically motivated objective reflects the actual movement. Furthermore, it turns out that neglecting markers close to the shoulder has less influence on the simulation results than neglecting markers close to the hand.

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Notes

  1. http://www.vicon.com/ (accessed September 17, 2016).

  2. http://www.qualisys.com/ (accessed September 17, 2016).

  3. http://www.optitrack.com/ (accessed September 17, 2016).

  4. movies available at http://www.ltd.tf.uni-erlangen.de/Research/Research.htm.

References

  1. Koschorreck, J., Mombaur, K.: Optimisation of somersaults and twists in platform diving. Comput. Methods Biomech. Biomed. Eng. 12(1), 157–159 (2009)

    Article  Google Scholar 

  2. Mombaur, K.: A mathematical study of sprinting on artificial legs. In: Bock, H.G., Hoang, X.P., Rannacher, R., Schlöder, J.P. (eds.) Modeling, Simulation and Optimization of Complex Processes, HPSC 2012, pp. 157–168. Springer, Berlin (2014). https://doi.org/10.1007/978-3-319-09063-4_13

    Chapter  Google Scholar 

  3. Mombaur, K.: Optimal Control for Applications in Medical and Rehabilitation Technology—Challenges and Solutions. Post-Conference Book of French-German-Polish Conference on Optimization. Springer, Berlin (2015)

    Google Scholar 

  4. Patel, S., Park, H., Bonato, P., Chan, L., Rodgers, M.: A review of wearable sensors and systems with application in rehabilitation. J. NeuroEng. Rehabil. 9, 21 (2012). https://doi.org/10.1186/1743-0003-9-21

    Article  Google Scholar 

  5. Vignais, N., Miezal, M., Bleser, G., Mura, K., Gorecky, D., Marin, F.: Innovative system for real-time ergonomic feedback in industrial manufacturing. Appl. Ergon. 44(4), 566–574 (2013). https://doi.org/10.1016/j.apergo.2012.11.008

    Article  Google Scholar 

  6. Bleser, G., Damen, D., Behera, A., Hendeby, G., Mura, K., Miezal, M., et al.: Cognitive learning, monitoring and assistance of industrial workflows using egocentric sensor networks. PLoS one 10(6), e0127769 (2015)

    Article  Google Scholar 

  7. Rettig, O., Fradet, L., Kasten, P., Raiss, P., Wolf, S.I.: A new kinematic model of the upper extremity based on functional joint parameter determination for shoulder and elbow. Gait Posture 30(4), 469–476 (2009)

    Article  Google Scholar 

  8. Ryu, T.: Application of soft tissue artifact compensation using displacement dependency between anatomical landmarks and skin markers. Anat. Res. Int. 2012, 123713 (2012)

    Google Scholar 

  9. De Rosario, H., Page, Á., Besa, A., Valera, Á.: Propagation of soft tissue artifacts to the center of rotation: a model for the correction of functional calibration techniques. J. Biomech. 46(15), 2619–2625 (2013)

    Article  Google Scholar 

  10. Hinze, M., Pinnau, R., Ulbrich, M., Ulbrich, S.: Optimization with PDE Constraints. Springer, Berlin (2009)

    MATH  Google Scholar 

  11. von Stryk, O., Bulirsch, R.: Direct and indirect methods for trajectory optimization. Annals of Operations Research 37, 357–373 (1992)

    Article  MathSciNet  Google Scholar 

  12. Betts, J.: Survey of numerical methods for trajectory optimization. J. Guid. Control Dyn. 21(2), 193–207 (1998)

    Article  Google Scholar 

  13. Cooper, J., Ballard, D.: Realtime physics-based marker following. In: Motion in Games. Lecture Notes in Computer Science, vol. 7660, pp. 350–361 (2012)

    Chapter  Google Scholar 

  14. Remy, C.D., Thelen, D.G: Optimal estimation of dynamically consistent kinematics and kinetics for forward dynamic simulation of gait. J. Biomech. Eng. 131(3), 031005 (2009). https://doi.org/10.1115/1.3005148

    Article  Google Scholar 

  15. Schwab, A., de Lange, P., Moore, J.: Rider optimal control identification in bicycling. In: Proceedings of the ASME 2012, 5th Annual Dynamic Systems and Control Conference, pp. 201–206 (2012). https://doi.org/10.1115/DSCC2012-MOVIC2012-8587

    Chapter  Google Scholar 

  16. Heinrich, D., Bogert, A., Nachbauer, W.: Relationship between jump landing kinematics and peak ACL force during a jump in downhill skiing: a simulation study. Scand. J. Med. Sci. Sports 24(3), e180–e187 (2014)

    Article  Google Scholar 

  17. Xiang, Y., Arora, J.S., Abdel-Malek, K.: Hybrid predictive dynamics: a new approach to simulate human motion. Multibody Syst. Dyn. 28(3), 199–224 (2012). https://doi.org/10.1007/s11044-012-9306-y

    Article  MathSciNet  Google Scholar 

  18. Xiang, Y., Arora, J.S., Abdel-Malek, K.: Optimization-based prediction of asymmetric human gait. J. Biomech. 44(4), 683–693 (2011). https://doi.org/10.1016/j.jbiomech.2010.10.045

    Article  Google Scholar 

  19. Maas, R., Leyendecker, S.: Biomechanical optimal control of human arm motion. Proc. Inst. Mech. Eng., Proc., Part K, J. Multi-Body Dyn. (2013). https://doi.org/10.1177/1464419313488363

    Article  Google Scholar 

  20. Leyendecker, S., Ober-Blöbaum, S., Marsden, J., Ortiz, M.: Discrete mechanics and optimal control for constrained systems. Optim. Control Appl. Methods 31(6), 505–528 (2010). https://doi.org/10.1002/oca.912

    Article  MathSciNet  MATH  Google Scholar 

  21. Ober-Blöbaum, S., Junge, O., Marsden, J.: Discrete mechanics and optimal control: an analysis. ESAIM Control Optim. Calc. Var. 17(2), 322–352 (2010). https://doi.org/10.1051/cocv/2010012

    Article  MathSciNet  MATH  Google Scholar 

  22. Betsch, P., Leyendecker, S.: The discrete null space method for the energy consistent integration of constrained mechanical systems. Part II: multibody dynamics. Int. J. Numer. Methods Eng. 67(4), 499–552 (2006)

    Article  Google Scholar 

  23. Marsden, J., West, M.: Discrete mechanics and variational integrators. Acta Numer. 10, 357–514 (2001)

    Article  MathSciNet  Google Scholar 

  24. Leyendecker, S., Marsden, J., Ortiz, M.: Variational integrators for constrained dynamical systems. Z. Angew. Math. Mech. 88(9), 677–708 (2008)

    Article  MathSciNet  Google Scholar 

  25. Gail, T., Hoffmann, R., Miezal, M., Bleser, G., Leyendecker, S.: Towards bridging the gap between motion capturing and biomechanical optimal control simulations. In: Proceedings of the Multibody Dynamics ECCOMAS Thematic Conference, Barcelona, Spain (2015). 10 pp.

    Google Scholar 

  26. Hatz, K., Schlöder, J., Bock, H.: Estimating parameters in optimal control problems. SIAM J. Sci. Comput. 34(3), A1707–A1728 (2012). https://doi.org/10.1137/110823390

    Article  MathSciNet  MATH  Google Scholar 

  27. Koch, M., Ringkamp, M., Leyendecker, S.: Discrete mechanics and optimal control (DMOCC) of walking gaits. J. Comput. Nonlinear Dyn. 12(2), 021006 (2016)

    Article  Google Scholar 

  28. Chandler, R.F., Reynolds, H.M., Young, J.W.: Investigation of inertial properties of the human body. Technical report, US Department of Transportation (1975)

  29. Uno, Y., Kawato, M., Suzuki, R.: Formation and control of optimal trajectory in human multijoint arm movement. Biol. Cybern. 61(2), 89–101 (1989)

    Article  Google Scholar 

  30. Betts, J.T.: Practical Methods for Optimal Control and Estimation Using Nonlinear Programming, 2nd edn. Cambridge University Press, New York (2009)

    MATH  Google Scholar 

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Acknowledgements

The authors acknowledge financial support by the German Research Foundation (LE 1841/2-1) and the German Federal Ministry of Education and Research (16SV7115, 03IHS075B).

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Correspondence to Sigrid Leyendecker.

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Hoffmann, R., Taetz, B., Miezal, M. et al. On optical data-guided optimal control simulations of human motion. Multibody Syst Dyn 48, 105–126 (2020). https://doi.org/10.1007/s11044-019-09701-4

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