Advertisement

Simulation and Visual Analysis of Neuromusculoskeletal Models and Data

  • Dimitar Stanev
  • Panagiotis Moschonas
  • Konstantinos Votis
  • Dimitrios Tzovaras
  • Konstantinos Moustakas
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 458)

Abstract

This work presents a novel medical decision support system for diseases related to the upper body neuromusculature. The backbone of the system is a simulation engine able to perform both forward and inverse simulation of upper limb motions. In forward mode neural signals are fed to the muscles that perform the corresponding motion. In the inverse mode, a specified motion trajectory is used as input and the neural signals that are the root cause of this particular motion are estimated and investigated. Due to the vast amount of information that results from even simple simulations, the results are presented to the expert using visual analytics metaphors and in particular both embodied and symbolic visualizations. Several use cases are presented so as to demonstrate the analytics potential of the proposed system.

Keywords

Neuromusculoskeletal simulation big data forward simulation inverse simulation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Buchanan, T., Lloyd, D., Manal, K., Besier, T.: Neuromusculoskeletal Modeling: Estimation of Muscle Forces and Joint Moments and Movements From Measurements of Neural Command. Journal of Applied Biomechanics 20(4), 367–395 (2006)CrossRefGoogle Scholar
  2. 2.
    Swan, M.: Emerging Patient-Driven Health Care Models: An Examination of Health Social Networks, Consumer Personalized Medicine and Quantified Self-Tracking. Int. J. Environ. Res. Public Health. 6(2), 492–525 (2009)CrossRefGoogle Scholar
  3. 3.
    Nitesh, C., Darcy, D.: Bringing Big Data to Personalized Healthcare: A Patient-Centered Framework. Journal of General Internal Medicine (2013)Google Scholar
  4. 4.
    Chen, H., Chiang, R., Storey, V.: Business intelligence and analytics from big data to big impact. Special Issue: Business Intelligence Research 36(4), 1165–1188 (2012)Google Scholar
  5. 5.
    Wong, P., Thomas, J.: Visual Analytics. IEEE Computer Graphics and Applications 24(5), 20–21 (2004)CrossRefGoogle Scholar
  6. 6.
    Keefe, F.: Integrating Visualisation and interaction research to improve scientific workflows. Computer Graphics and Applications IEEE 30(2), 8–13 (2010)CrossRefGoogle Scholar
  7. 7.
    Spurlock, S., Chang, R., Wang, X., Arceneaux, G., Keefe, F., Souvenir, R.: Combining automated and interactive visual analysis of biomechanical motion data. Advances in Visual Computing, 564–573 (2010)Google Scholar
  8. 8.
    Drury, G.: Human factors/ergonomics implications of big data analytics: Chartered Institute of Ergonomics and Human Factors annual lecture. Ergonomics (ahead-of-print), 1–15 (2015)Google Scholar
  9. 9.
    Vaquero, M., Rzepecki, J., Friese, I., Wolter, E.: Visualisation and user interaction methods for multiscale biomedical data. 3D Multiscale Physiological Human, 107–133 (2014)Google Scholar
  10. 10.
    Huan, T., Wu, X., Chen, Y.: Systems biology Visualisation tools for drug target discovery. Expert Opinion on Drug Discovery 5(5), 425–439 (2010)CrossRefGoogle Scholar
  11. 11.
    Hicks, J., Uchida, T., Seth, A., Rajagopal, A., Delp, S.: Is my model good enough? Best practices for verification and validation of musculoskeletal models and simulations of human movement. Journal of Biomechanical Engineering 137(2), 1–24 (2014)Google Scholar
  12. 12.
    Fregly, B., Besier, T., Lloyd, D., Delp, S., Banks, S., Pandy, M., D’Lima, D.: Grand challenge competition to predict in vivo knee loads. Journal of Orthopaedic Research 30(4), 503–513 (2012)CrossRefGoogle Scholar
  13. 13.
    Pandy, M.: Computer Modeling and Simulation of Human Movement. Annals of Biomedical Engineering 3, 245–273 (2001)CrossRefGoogle Scholar
  14. 14.
    Millard, M., Uchida, T., Seth, A., Delp, S.: Flexing Computational Muscle: Modeling and Simulation of Musculotendon Dynamics. Journal of Biomechanical Engineering 135(2), 1–12 (2013)CrossRefGoogle Scholar
  15. 15.
    Erdemir, A., Lean, S., Herzog, W., Bogert, A.: Model-based estimation of muscle forces exerted during movements. Clinical Biomechanics 22(2), 131–154 (2007)CrossRefGoogle Scholar
  16. 16.
    Thelen, D., Anderson, F.: Using computed muscle control to generate forward dynamic simulations of human walking from experimental data. Journal of Biomechanics 39(6), 1107–1115 (2006)CrossRefGoogle Scholar
  17. 17.
    Anderson, F., Pandy, M.: Static and dynamic optimization solutions for gait are practically equivalent. Journal of Biomechanics 34(2), 153–161 (2001)CrossRefGoogle Scholar
  18. 18.
    Delp, S., Anderson, F., Arnold, A., Loan, P., Habib, A., John, C., Guendelman, E., Thelen, D.: OpenSim: Open-Source Software to Create and Analyze Dynamic Simulations of Movement. IEEE Transactions on Biomedical Engineering 54(11), 1940–1950 (2007)CrossRefGoogle Scholar
  19. 19.
    Holzbaur, K., Murray, W., Delp, S.: A model of the upper extremity for simulating musculoskeletal surgery and analyzing neuromuscular control. Annals of Biomedical Engineering 33(6), 829–840 (2005)CrossRefGoogle Scholar
  20. 20.
    Saul, K., Hu, X., Goehler, C., Vidt, M., Daly, M., Velisar, A., Murray, W.: Computer methods in biomechanics and biomedical engineering, 1–14 (2014)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2015

Authors and Affiliations

  • Dimitar Stanev
    • 1
  • Panagiotis Moschonas
    • 2
  • Konstantinos Votis
    • 2
  • Dimitrios Tzovaras
    • 2
  • Konstantinos Moustakas
    • 1
  1. 1.University of PatrasPatraGreece
  2. 2.Center for Research and Technology Hellas (CERTH)ThessalonikiGreece

Personalised recommendations