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.
Chapter PDF
Similar content being viewed by others
References
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)
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)
Nitesh, C., Darcy, D.: Bringing Big Data to Personalized Healthcare: A Patient-Centered Framework. Journal of General Internal Medicine (2013)
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)
Wong, P., Thomas, J.: Visual Analytics. IEEE Computer Graphics and Applications 24(5), 20–21 (2004)
Keefe, F.: Integrating Visualisation and interaction research to improve scientific workflows. Computer Graphics and Applications IEEE 30(2), 8–13 (2010)
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)
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)
Vaquero, M., Rzepecki, J., Friese, I., Wolter, E.: Visualisation and user interaction methods for multiscale biomedical data. 3D Multiscale Physiological Human, 107–133 (2014)
Huan, T., Wu, X., Chen, Y.: Systems biology Visualisation tools for drug target discovery. Expert Opinion on Drug Discovery 5(5), 425–439 (2010)
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)
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)
Pandy, M.: Computer Modeling and Simulation of Human Movement. Annals of Biomedical Engineering 3, 245–273 (2001)
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)
Erdemir, A., Lean, S., Herzog, W., Bogert, A.: Model-based estimation of muscle forces exerted during movements. Clinical Biomechanics 22(2), 131–154 (2007)
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)
Anderson, F., Pandy, M.: Static and dynamic optimization solutions for gait are practically equivalent. Journal of Biomechanics 34(2), 153–161 (2001)
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)
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)
Saul, K., Hu, X., Goehler, C., Vidt, M., Daly, M., Velisar, A., Murray, W.: Computer methods in biomechanics and biomedical engineering, 1–14 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 IFIP International Federation for Information Processing
About this paper
Cite this paper
Stanev, D., Moschonas, P., Votis, K., Tzovaras, D., Moustakas, K. (2015). Simulation and Visual Analysis of Neuromusculoskeletal Models and Data. In: Chbeir, R., Manolopoulos, Y., Maglogiannis, I., Alhajj, R. (eds) Artificial Intelligence Applications and Innovations. AIAI 2015. IFIP Advances in Information and Communication Technology, vol 458. Springer, Cham. https://doi.org/10.1007/978-3-319-23868-5_29
Download citation
DOI: https://doi.org/10.1007/978-3-319-23868-5_29
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-23867-8
Online ISBN: 978-3-319-23868-5
eBook Packages: Computer ScienceComputer Science (R0)