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Classification of Nonlinear Dynamics of Human Posture Using Support Vector Machines

[+] Author Affiliations
Amit Shukla

Miami University, Oxford, OH

Paper No. DETC2013-13609, pp. V07AT10A016; 8 pages
  • ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 7A: 9th International Conference on Multibody Systems, Nonlinear Dynamics, and Control
  • Portland, Oregon, USA, August 4–7, 2013
  • Conference Sponsors: Design Engineering Division, Computers and Information in Engineering Division
  • ISBN: 978-0-7918-5596-6
  • Copyright © 2013 by ASME


Human postural control can be modeled as a complex, non-linear multi-sensory and multi actuation system. Postural control requires a balance between various sensory inputs and the corresponding response in a motor control framework. Various motor control disorders result in a propensity for fall. In this paper, a classification scheme using support vector machines (SVM) is investigated to classify individuals between healthy and those prone to balance disorders including falls. First, a computational model is described to illustrate the proposed approach. It is shown that using SVM, in conjunction with L2 Norm associated with the phase space trajectories, it is possible to classify fallers and non-fallers. This metric can then be utilized for clinical application upon further evaluation. This may benefit clinical evaluation of Parkinson’s subjects by complementing the widely used UPDRS scale.

Copyright © 2013 by ASME



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