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Expectation Maximization Method to Identify an Electrically Stimulated Musculoskeletal Model

[+] Author Affiliations
Harish Ravichandar, Ashwin Dani, Jacquelyn Khadijah-Hajdu

University of Connecticut, Storrs, CT

Nicholas Kirsch, Qiang Zhong, Nitin Sharma

University of Pittsburgh, Pittsburgh, PA

Paper No. DSCC2015-9956, pp. V002T23A009; 7 pages
  • ASME 2015 Dynamic Systems and Control Conference
  • Volume 2: Diagnostics and Detection; Drilling; Dynamics and Control of Wind Energy Systems; Energy Harvesting; Estimation and Identification; Flexible and Smart Structure Control; Fuels Cells/Energy Storage; Human Robot Interaction; HVAC Building Energy Management; Industrial Applications; Intelligent Transportation Systems; Manufacturing; Mechatronics; Modelling and Validation; Motion and Vibration Control Applications
  • Columbus, Ohio, USA, October 28–30, 2015
  • Conference Sponsors: Dynamic Systems and Control Division
  • ISBN: 978-0-7918-5725-0
  • Copyright © 2015 by ASME


A system identification algorithm for a musculoskeletal system using an approximate expectation maximization (E-M) is presented. Effective control design for neuroprosthesis applications necessitates a well defined muscle model. A dynamic model of the lower leg with a fixed ankle is considered. The unknown parameters of the model are estimated using an approximate E-M algorithm based on knee angle measurements collected from an able-bodied subject during stimulated knee extension. The parameters estimated from the data are compared to reference values obtained by conducting experiments that separate the parameters in the dynamics from one another. The presented results demonstrate the capability of the proposed algorithm to identify the parameters of the dynamic model from knee angle measurements.

Copyright © 2015 by ASME



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